U.S. patent application number 15/169719 was filed with the patent office on 2017-01-12 for fast scaning based on magnetic resonance history.
This patent application is currently assigned to Tesla Health, Inc. The applicant listed for this patent is Tesla Health, Inc. Invention is credited to Jeffrey Howard Kaditz, Andrew Gettings Stevens.
Application Number | 20170007148 15/169719 |
Document ID | / |
Family ID | 57686021 |
Filed Date | 2017-01-12 |
United States Patent
Application |
20170007148 |
Kind Code |
A1 |
Kaditz; Jeffrey Howard ; et
al. |
January 12, 2017 |
FAST SCANING BASED ON MAGNETIC RESONANCE HISTORY
Abstract
During operation, a system iteratively captures MR signals of
one or more types of nuclei in one or more portions of a biological
lifeform based on scanning instructions that correspond to a
dynamic scan plan. The MR signals in a given iteration may be
associated with voxels having associated sizes at three-dimensional
(3D) positions in at least a corresponding portion of the
biological lifeform. If the system detects a potential anomaly when
analyzing the MR signals from the given iteration, the system
dynamically modifies the scan plan based on the detected potential
anomaly, a medical history and/or an MR-scan history. Subsequent
measurements of MR signals may be associated with the same or
different: types of nuclei, portions of the biological lifeform,
voxels sizes and/or 3D positions.
Inventors: |
Kaditz; Jeffrey Howard;
(Wilson, WY) ; Stevens; Andrew Gettings; (New
York, NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Tesla Health, Inc |
Millbrae |
CA |
US |
|
|
Assignee: |
Tesla Health, Inc
Millbrae
CA
|
Family ID: |
57686021 |
Appl. No.: |
15/169719 |
Filed: |
May 31, 2016 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
62189675 |
Jul 7, 2015 |
|
|
|
62233291 |
Sep 25, 2015 |
|
|
|
62233288 |
Sep 25, 2015 |
|
|
|
62245269 |
Oct 22, 2015 |
|
|
|
62250501 |
Nov 3, 2015 |
|
|
|
62253128 |
Nov 9, 2015 |
|
|
|
62255363 |
Nov 13, 2015 |
|
|
|
62281176 |
Jan 20, 2016 |
|
|
|
62213625 |
Sep 3, 2015 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/7207 20130101;
A61B 5/055 20130101; A61B 5/7282 20130101; A61B 5/7267 20130101;
A61B 5/7221 20130101 |
International
Class: |
A61B 5/055 20060101
A61B005/055; A61B 5/00 20060101 A61B005/00 |
Claims
1. A system to perform a magnetic-resonance (MR) scan, comprising:
an MR scanner that, during operation, is configured to perform one
or more MR scans of at least a first portion of a biological
lifeform; an interface circuit electrically coupled to the MR
scanner, wherein, during operation, the interface circuit is
configured to communicate information with the MR scanner, and
wherein, during operation, the system is configured to: provide, to
the MR scanner, first scanning instructions based on an initial
scan plan to capture first MR signals of one or more first types of
nuclei in at least the first portion of the biological lifeform,
wherein the first MR signals are associated with first voxels
having first sizes at first three-dimensional (3D) positions in at
least the first portion of the biological lifeform; receive, from
the MR scanner, the first MR signals; analyze the first MR signals
to detect a potential anomaly in the first MR signals based on one
or more of: a medical history of the biological lifeform; an
MR-scan history of the biological lifeform that includes prior MR
scans of the biological lifeform; and a first template of a
potential anomaly; dynamically modify the initial scan plan based
on one or more of the detected potential anomaly, the medical
history and the MR-scan history, wherein the modified scan plan
includes one or more second types of nuclei in second voxels,
having associated second sizes, in at least a second portion of the
biological lifeform, and wherein the second sizes are different
than the first sizes; provide, to the MR scanner, second scanning
instructions based on the modified scan plan to capture second MR
signals of the one or more second types of nuclei in at least the
second portion of the biological lifeform, wherein the second MR
signals are associated with the second voxels at second 3D
positions in at least the second portion of the biological
lifeform; and receive, from the MR scanner, the second MR
signals.
2. The system of claim 1, wherein, during operation, the system is
further configured to generate the initial scan plan for at least
the first portion of the biological lifeform based on the medical
history and the MR-scan history; and wherein the initial scan plan
includes the one or more first types of nuclei in the first voxels,
having the first sizes, in at least the first portion of the
biological lifeform.
3. The system of claim 1, wherein the first template of the
potential anomaly includes simulated MR signals of the one or more
first types of nuclei at the first voxels in at least the
biological lifeform.
4. The system of claim 3, wherein, during operation, the system is
further configured to generate the simulated MR signals.
5. The system of claim 3, wherein generating the simulated MR
signals involves one of: resampling predetermined MR signals;
interpolating the predetermined MR signals at the first voxels; and
calculating the simulated MR signals using a previously determined
invariant MR signature, predetermined characteristics of the MR
scanner and the initial scanning instructions.
6. The system of claim 1, wherein, during operation, the system is
further configured to classify each of the voxels associated with
the detected potential anomaly as having one of: a risk of
misclassification that is less than a threshold value; the risk
misclassification that is greater than the threshold value; and the
risk misclassification that is unknown.
7. The system of claim 1, wherein at least the second portion of
the biological lifeform corresponds to the 3D positions of the
detected potential anomaly.
8. The system of claim 7, wherein the second voxels sizes and at
least the second portion of the biological lifeform are computed
from a size of the detected potential anomaly.
9. The system of claim 1, wherein, during operation, the system is
further configured to analyze the second MR signals to refine the
detected potential anomaly based on one or more of: the medical
history; the MR-scan history; and a second template of the
potential anomaly.
10. The system of claim 9, wherein the second template of the
potential anomaly includes simulated MR signals of the one or more
second types of nuclei at the second voxels in at least the
biological lifeform.
11. The system of claim 1, wherein the first MR signals include a
first MR image and the second MR signals include a second MR
image.
12. The system of claim 1, wherein the second scanning instructions
correspond to one of: magnetic-resonance spectroscopy (MRS),
magnetic-resonance thermometry (MRT), magnetic-resonance
elastography (MRE), MR fingerprinting, and diffusion-tensor
imaging.
13. The system of claim 1, wherein, during operation, the system is
further configured to analyze adjacent voxels associated with the
detected potential anomaly to determine a metabolic chemical
signature in magnetic-resonance spectroscopy (MRS).
14. The system of claim 1, wherein analyzing the first MR signals
involves aligning the first MR signals in the first voxels with
anatomical landmarks of the biological lifeform in a prior MR scan
of the biological lifeform and comparing the aligned first MR
signals in the first voxels with prior first MR signals in the
first voxels in the prior MR scan.
15. The system of claim 1, wherein the second voxel sizes and at
least the second portion of the biological lifeform are based on a
location in the biological lifeform of the potential anomaly.
16. The system of claim 1, wherein, during operation, the system is
further configured to: provide, to the MR scanner, third scanning
instructions based on the initial scan plan to capture third MR
signals of the one or more first types of nuclei in a third portion
of the biological lifeform, wherein the third MR signals are
associated with the first voxels at third 3D positions in at least
the third portion of the biological lifeform; and receive, from the
MR scanner, the third MR signals, wherein the third MR signals
complete the initial scan plan that was interrupted to capture the
second MR signals.
17. The system of claim 1, wherein, during operation, the system is
further configured to determine a recommended time for a subsequent
MR scan of the biological lifeform based on one or more of: the
medical history; the MR-scan history; and the detected potential
anomaly.
18. The system of claim 1, wherein the initial scan plan is
dynamically modified based on detection of the potential anomaly or
another potential anomaly in a second biological lifeform.
19. A computer-program product for use in conjunction with a
magnetic-resonance (MR) scanner, the computer-program product
comprising a non-transitory computer-readable storage medium and a
computer-program mechanism embedded therein to perform an MR scan,
the computer program mechanism including instructions for:
providing, to the MR scanner, first scanning instructions based on
an initial scan plan to capture first MR signals of one or more
first types of nuclei in at least the first portion of the
biological lifeform, wherein the first MR signals are associated
with first voxels having first sizes at first three-dimensional
(3D) positions in at least the first portion of the biological
lifeform; receiving, from the MR scanner, the first MR signals;
analyzing the first MR signals to detect a potential anomaly in the
first MR signals based on one or more of: a medical history of the
biological lifeform; an MR-scan history of the biological lifeform
that includes prior MR scans of the biological lifeform; and a
first template of a potential anomaly; dynamically modifying the
initial scan plan based on one or more of the detected potential
anomaly, the medical history and the MR-scan history, wherein the
modified scan plan includes one or more second types of nuclei in
second voxels, having associated second sizes, in at least a second
portion of the biological lifeform, and wherein the second sizes
are different than the first sizes; providing, to the MR scanner,
second scanning instructions based on the modified scan plan to
capture second MR signals of the one or more second types of nuclei
in at least the second portion of the biological lifeform, wherein
the second MR signals are associated with the second voxels at
second 3D positions in at least the second portion of the
biological lifeform; and receiving, from the MR scanner, the second
MR signals.
20. A computer-implemented method for performing a
magnetic-resonance (MR) scan using an MR scanner, the method
comprising: providing, to the MR scanner, first scanning
instructions based on an initial scan plan to capture first MR
signals of one or more first types of nuclei in at least the first
portion of the biological lifeform, wherein the first MR signals
are associated with first voxels having first sizes at first
three-dimensional (3D) positions in at least the first portion of
the biological lifeform; receiving, from the MR scanner, the first
MR signals; analyzing the first MR signals to detect a potential
anomaly in the first MR signals based on one or more of: a medical
history of the biological lifeform; an MR-scan history of the
biological lifeform that includes prior MR scans of the biological
lifeform; and a first template of a potential anomaly; dynamically
modifying the initial scan plan based on one or more of the
detected potential anomaly, the medical history and the MR-scan
history, wherein the modified scan plan includes one or more second
types of nuclei in second voxels, having associated second sizes,
in at least a second portion of the biological lifeform, and
wherein the second sizes are different than the first sizes;
providing, to the MR scanner, second scanning instructions based on
the modified scan plan to capture second MR signals of the one or
more second types of nuclei in at least the second portion of the
biological lifeform, wherein the second MR signals are associated
with the second voxels at second 3D positions in at least the
second portion of the biological lifeform; and receiving, from the
MR scanner, the second MR signals.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] The is application claims priority under 35 U.S.C.
.sctn.119(e) to: U.S. Provisional Application Ser. No. 62/189,675,
entitled "Systems and Method for Indexed Medical Imaging of a
Subject Over Time," by Jeffrey H. Kaditz and Andrew G. Stevens,
Attorney Docket Number TSLH-P01.00, filed on Jul. 7, 2015; U.S.
Provisional Application Ser. No. 62/213,625, entitled "Systems and
Method for Indexed Medical Imaging of a Subject Over Time," by
Jeffrey H. Kaditz and Andrew G. Stevens, Attorney Docket Number
TSLH-P01.01, filed on Sep. 3, 2015; U.S. Provisional Application
Ser. No. 62/233,291, entitled "Systems and Method for Indexed
Medical Imaging of a Subject Over Time," by Jeffrey H. Kaditz and
Andrew G. Stevens, Attorney Docket Number TSLH-P01.02, filed on
Sep. 25, 2015; U.S. Provisional Application Ser. No. 62/233,288,
entitled "Systems and Method for Indexed Medical and/or
Fingerprinting Tissue," by Jeffrey H. Kaditz and Andrew G. Stevens,
Attorney Docket Number TSLH-P08.00, filed on Sep. 25, 2015; U.S.
Provisional Application Ser. No. 62/245,269, entitled "System and
Method for Auto Segmentation and Generalized MRF with Minimized
Parametric Mapping Error Using A Priori Knowledge," by Jeffrey H.
Kaditz, Attorney Docket Number TSLH-P10.00, filed on Oct. 22, 2015;
U.S. Provisional Application Ser. No. 62/250,501, entitled "System
and Method for Auto Segmentation and Generalized MRF with Minimized
Parametric Mapping Error Using A Priori Knowledge," by Jeffrey H.
Kaditz, Attorney Docket Number TSLH-P10.01, filed on Nov. 3, 2015;
U.S. Provisional Application Ser. No. 62/253,128, entitled "System
and Method for Auto Segmentation and Generalized MRF with Minimized
Parametric Mapping Error Using A Priori Knowledge," by Jeffrey H.
Kaditz, Attorney Docket Number TSLH-P10.02, filed on Nov. 9, 2015;
U.S. Provisional Application Ser. No. 62/255,363, entitled "System
and Method for Auto Segmentation and Generalized MRF with Minimized
Parametric Mapping Error Using A Priori Knowledge," by Jeffrey H.
Kaditz, Attorney Docket Number TSLH-P10.03, filed on Nov. 13, 2015;
and U.S. Provisional Application Ser. No. 62/281,176, entitled
"System and Method for Auto Segmentation and Generalized MRF with
Minimized Parametric Mapping Error Using A Priori Knowledge," by
Jeffrey H. Kaditz, Attorney Docket Number TSLH-P10.04, filed on
Jan. 20, 2016, the contents of each of which are herein
incorporated by reference.
BACKGROUND
[0002] Field
[0003] The described embodiments relate generally magnetic
resonance (MR), more specifically to performing MR scans based on
longitudinal MR histories of one or more individuals and/or medical
histories of the individuals. More generally, the described
embodiments relate to performing non-invasive medical imaging (such
as computed tomography, ultrasound or MR imaging) based on
longitudinal imaging histories of the one or more individuals
and/or the medical histories of the individuals.
[0004] Related Art
[0005] Magnetic resonance or MR (which is often referred to as
`nuclear magnetic resonance` or NMR) is a physical phenomenon in
which nuclei in a magnetic field absorb and re-emit electromagnetic
radiation. For example, magnetic nuclear spins may be partially
aligned (or polarized) in an applied external magnetic field. These
nuclear spins may precess or rotate around the direction of the
external magnetic field at an angular frequency (which is sometimes
referred to as the `Larmor frequency`) given by the product of a
gyromagnetic ratio of a type of nuclei and the magnitude or
strength of the external magnetic field. By applying a perturbation
to the polarized nuclear spins, such as one or more radio-frequency
(RF) pulses (and, more generally, electro-magnetic pulses) having
pulse widths corresponding to the angular frequency and at a
right-angle or perpendicular to the direction of the external
magnetic field, the polarization of the nuclear spins can be
transiently changed. The resulting dynamic response of the nuclear
spins (such as the time-varying total magnetization) can provide a
wealth of information about the physical and material properties of
a sample.
[0006] In medicine, MR has been widely used to non-invasively
determine anatomical structure and/or the chemical composition of
different types of tissue. For example, in magnetic resonance
imaging (MRI), the dependence of the angular frequency of
precession of nuclear spins (such as protons or the isotope
.sup.1H) on the magnitude of the external magnetic field is used to
determine images of anatomical structure. In particular, by
applying a non-uniform or spatially varying magnetic field to a
patient, the resulting variation in the angular frequency of
precession of .sup.1H spins is typically used to spatially localize
the measured dynamic response of the .sup.1H spins to voxels, which
can be used to generate images of the internal anatomy of the
patient.
[0007] However, existing approaches to MRI are typically
time-consuming. For example, acquiring MR images with high-spatial
resolution (i.e., small voxels sizes) often involves a large number
of measurements (which are sometimes referred to as `scans`) to be
performed. Moreover, in order to achieve high-spatial resolution, a
large homogenous external magnetic field is usually used during
MRI. The external magnetic field is typically generated using a
superconducting magnetic having a toroidal shape with a narrow
bore, which can feel confining to many patients.
[0008] The combination of long scan times and the confining
environment of the magnet bore can degrade the user experience
during MRI. Indeed, some patients feel profoundly claustrophobic in
MR scanners. In addition, long scan times reduce throughput,
thereby increasing the cost of performing MM.
SUMMARY
[0009] Some embodiments relate to a system that performs an MR
scan. This system includes: an MR scanner that, during operation,
performs one or more MR scans of at least a first portion of a
biological lifeform; and an interface circuit that, during
operation, communicates information with the MR scanner. Moreover,
during operation, the system: provides, to the MR scanner, first
scanning instructions based on an initial scan plan to capture
first MR signals of one or more first types of nuclei in at least
the first portion of the biological lifeform, where the first MR
signals are associated with first voxels having first sizes at
first three-dimensional (3D) positions in at least the first
portion of the biological lifeform; receives, from the MR scanner,
the first MR signals; and analyzes the first MR signals to detect a
potential anomaly in the first MR signals based on: a medical
history of the biological lifeform; an MR-scan history of the
biological lifeform that includes prior MR scans of the biological
lifeform; and/or a first template of a potential anomaly.
Furthermore, the system dynamically modifies the initial scan plan
based on the detected potential anomaly, the medical history and/or
the MR-scan history, where the modified scan plan includes one or
more second types of nuclei in second voxels, having associated
second sizes, in at least a second portion of the biological
lifeform, and where the second sizes are different than the first
sizes. Additionally, the system: provides, to the MR scanner,
second scanning instructions based on the modified scan plan to
capture second MR signals of the one or more second types of nuclei
in at least the second portion of the biological lifeform, where
the second MR signals are associated with the second voxels at
second 3D positions in at least the second portion of the
biological lifeform; and receives, from the MR scanner, the second
MR signals.
[0010] In some embodiments, the system generates the initial scan
plan for at least the first portion of the biological lifeform
based on the medical history and the MR-scan history, where the
initial scan plan may include the one or more first types of nuclei
in the first voxels, having the first sizes, in at least the first
portion of the biological lifeform.
[0011] Note that the first template of the potential anomaly may
include simulated MR signals of the one or more first types of
nuclei at the first voxels in at least the biological lifeform. In
some embodiments, the system generates the simulated MR signals.
For example, the generating of the simulated MR signals may
involve: resampling predetermined MR signals; interpolating the
predetermined simulated MR signals at the first voxels; and/or
calculating the simulated MR signals using a previously determined
invariant MR signature, predetermined characteristics of the MR
scanner and the initial scanning instructions.
[0012] Moreover, the system may classify each of the voxels
associated with the detected potential anomaly as having: a risk of
misclassification that is less than a threshold value; the risk
misclassification that is greater than the threshold value; and/or
the risk misclassification that is unknown. Note that at least the
second portion of the biological lifeform may correspond to the 3D
positions of the detected potential anomaly. Furthermore, the
second voxels sizes and at least the second portion of the
biological lifeform may be computed from a size of the detected
potential anomaly. In some embodiments, the system updates, based
on additional information (such as additional MR scans on the same
or another biological lifeform, etc.) the classification; and
changes a recommended time for a subsequent MR scan based on the
updated classification.
[0013] Additionally, the system may analyze the second MR signals
to refine the detected potential anomaly based on one or more of:
the medical history; the MR-scan history; and/or a second template
of the potential anomaly. Note that the second template of the
potential anomaly may include simulated MR signals of the one or
more second types of nuclei at the second voxels in at least the
biological lifeform.
[0014] Note that the first MR signals may include a first MR image
and the second MR signals may include a second MR image.
[0015] Moreover, the second scanning instructions may correspond
to: magnetic-resonance spectroscopy (MRS), magnetic-resonance
thermometry (MRT), magnetic-resonance elastography (MRE), MR
fingerprinting (MRF), and diffusion-tensor imaging.
[0016] Furthermore, the system may analyze adjacent voxels
associated with the detected potential anomaly to determine a
metabolic chemical signature in MRS. In some embodiments, the
analysis of the first MR signals includes instructions for aligning
the first MR signals in the first voxels with anatomical landmarks
of the biological lifeform in a prior MR scan of the biological
lifeform and comparing the aligned first MR signals in the first
voxels with prior first MR signals in the first voxels in the prior
MR scan. For example, the aligning may involve performing point-set
registration.
[0017] Note that the second voxel sizes and at least the second
portion of the biological lifeform may be based on a location in
the biological lifeform of the potential anomaly.
[0018] Additionally, the system may: provide, to the MR scanner,
third scanning instructions based on the initial scan plan to
capture third MR signals of the one or more first types of nuclei
in a third portion of the biological lifeform, where the third MR
signals are associated with the first voxels at third 3D positions
in at least the third portion of the biological lifeform; and
receive, from the MR scanner, the third MR signals, where the third
MR signals complete the initial scan plan that was interrupted to
capture the second MR signals.
[0019] Moreover, the system may determine the recommended time for
a subsequent MR scan of the biological lifeform based on one or
more of: the medical history; the MR-scan history; and the detected
potential anomaly.
[0020] Furthermore, the system may dynamically modify the initial
scan plan based on detection of the potential anomaly or another
potential anomaly in a second biological lifeform.
[0021] In some embodiments, the system includes a processor and
memory that stores a program module. During operation, the
processor executes the program module to perform scans of the
biological lifeform.
[0022] Another embodiment provides a computer-program product for
use with an MR scanner. This computer-program product includes
instructions for at least some of the aforementioned operations
performed by the system.
[0023] Another embodiment provides a method for performing an MR
scan using an MR scanner. This method includes at least some of the
aforementioned operations performed by the system.
[0024] This Summary is provided for purposes of illustrating some
exemplary embodiments, so as to provide a basic understanding of
some aspects of the subject matter described herein. Accordingly,
it will be appreciated that the above-described features are simply
examples and should not be construed to narrow the scope or spirit
of the subject matter described herein in any way. Other features,
aspects, and advantages of the subject matter described herein will
become apparent from the following Detailed Description, Figures,
and Claims.
BRIEF DESCRIPTION OF THE FIGURES
[0025] FIG. 1 is a block diagram illustrating a system with a
magnetic-resonance (MR) scanner that performs an MR scan of a
biological lifeform in accordance with an embodiment of the present
disclosure.
[0026] FIG. 2 is a block diagram of the MR scanner in the system of
FIG. 1 in accordance with an embodiment of the present
disclosure.
[0027] FIG. 3 is a drawing illustrating the determination of an MR
model in accordance with an embodiment of the present
disclosure.
[0028] FIG. 4 is a drawing illustrating a set of MR signals that
specify the response to a surface of magnetic-field strengths in
accordance with an embodiment of the present disclosure.
[0029] FIG. 5 is a flow diagram illustrating a method for
performing an MR scan in accordance with an embodiment of the
present disclosure.
[0030] FIG. 6 is a drawing illustrating communication among
components in the system in FIG. 1 in accordance with an embodiment
of the present disclosure.
[0031] FIG. 7 is a drawing of a voxel and offset voxels
illustrating an example of upsampling of individual voxels.
[0032] FIG. 8 is a block diagram illustrating an electronic device
in the system of FIG. 1 in accordance with an embodiment of the
present disclosure.
[0033] FIG. 9 is a drawing illustrating a data structure that is
used by the electronic device of FIG. 8 in accordance with an
embodiment of the present disclosure.
[0034] Table 1 provides spin-lattice (T.sub.1) and spin-spin
(T.sub.2) relaxation times in different types of tissue in
accordance with an embodiment of the present disclosure.
[0035] Note that like reference numerals refer to corresponding
parts throughout the drawings. Moreover, multiple instances of the
same part are designated by a common prefix separated from an
instance number by a dash.
DETAILED DESCRIPTION
[0036] During operation, a system iteratively captures MR signals
of one or more types of nuclei in one or more portions of a
biological lifeform (such as a person) based on scanning
instructions that correspond to a dynamic scan plan. The MR signals
in a given iteration may be associated with voxels having
associated sizes at 3D positions in at least a corresponding
portion of the biological lifeform. If the system detects a
potential anomaly when analyzing the MR signals from the given
iteration, the system dynamically modifies the scan plan based on
the detected potential anomaly, a medical history and/or an MR-scan
history. Subsequent measurements of MR signals may be associated
with the same or different: types of nuclei, portions of the
biological lifeform, voxel sizes and/or 3D positions.
[0037] By dynamically updating the scan plan (and, thus, the
acquired or captured MR signals), this measurement technique may
facilitate fast MR scans. For example, an initial MR scan may use
an initial voxel size, and a subsequent MR scan may use a finer
voxel size in a portion of the biological lifeform that is of
interest, such as a specific anatomical region where a potential
anomaly was detected. Note that the voxel size(s) may or may not be
isometric. Moreover, instead of voxels, imaging can be performed
using tomographic slicing.
[0038] In addition, the measurement technique may allow the scan
plan to be updated based on the medical history of one or more
biological lifeforms and/or the scan history of the one or more
biological lifeforms, which may include one or more prior MR scans
of the one or more biological lifeforms. This approach may allow
knowledge obtained for the same and/or different individuals to be
used to perform the MR scans in an intelligent manner. For example,
the one or more prior MR scans (which were performed on another
occasion) and/or the medical history may allow the specific medical
circumstances of an individual to be determined and used to guide
subsequent MR scans. Over time, therefore, the measurement
technique may allow increased focus (e.g., at higher resolution) at
one or more predicted regions of interest in an individual. The one
or more prior MR scans may also be used as a quantitative baseline
during analysis of the subsequent MR scans, which may improve the
accuracy of the analysis and may reduce the time and the
signal-to-noise ratio (SNR) needed for accurate detection of a
potential anomaly.
[0039] Consequently, the measurement technique may reduce the time
and, thus, may increase the throughput associated with MR scans,
such as in MRI and/or another MR technique. The increased
throughput may significantly reduce the cost of the MR scans.
Moreover, the reduction in the scan time may improve the user
experience by reducing the amount of time people spend in the
confining environment of a magnet bore in an MR scanner. In
addition, the use of a quantitative baseline may facilitate
quantitative analysis of the MR scans and may improve the accuracy
of the MR scans, which may reduce medical errors, thereby improving
the health and well-being of people.
[0040] Note that the quantitative analysis of the MR scans in the
measurement technique may be facilitated by the use of MR
fingerprints of biological lifeforms that are magnetic-field
invariant (which are sometimes referred to as
`magnetic-field-invariant MR signatures` or `invariant MR
signatures`). The invariant MR signatures may describe the dynamic
MR responses of voxels at 3D positions in the one or more
biological lifeforms at arbitrary magnetic-field strengths.
Moreover, the invariant MR signatures may be independent of the MR
scanners, as well as the specific scanning instructions (e.g.,
magnetic-field strengths and/or pulse sequences), used to acquire
MR signals in a variation on MRF (which is sometimes referred to as
`quantitative MRF` or QMR-X) that were then used to determine the
invariant MR signatures. As described further below, an invariant
MR signature may be determined by iteratively converging MR signals
of one or more types of nuclei in a biological lifeform, which were
acquired by an MR scanner based on scanning instructions, with
simulated MR signals (which are sometimes referred to as calculated
MR signals or estimated MR signals) for the biological lifeform
that are generated using an MR model and the scanning
instructions.
[0041] In the discussion that follows, the measurement technique
may be used in conjunction with a variety of MR techniques,
including: MRI, MRS, magnetic resonance spectral imaging (MRSI),
MRF, MRE, MRT, magnetic-field relaxometry, diffusion-tensor imaging
and/or another MR technique (such as functional MRI, metabolic
imaging, molecular imaging, blood-flow imaging, etc.). Note that
these MR techniques are each a form of quantitative tensor-field
mapping.
[0042] In particular, `MRI` should be understood to include
generating images (such as 2D slices) or maps of internal structure
in a sample (such as anatomical structure in a biological sample,
e.g., a tissue sample or a patient) based on the dynamic response
of a type of nuclear spin (such protons or the isotope .sup.1H) in
the presence of a magnetic field, such as a non-uniform or
spatially varying external magnetic field (e.g., an external
magnetic field with a well-defined spatial gradient). Moreover, MRS
should be understood to include determining chemical composition or
morphology of a sample (such as a biological sample) based on the
dynamic response of multiple types of nuclear spins (other than or
in addition to .sup.1H) in the presence of a magnetic field, such
as a uniform external magnetic field.
[0043] Furthermore, MRST should be understood to include generating
images or maps of internal structure and/or chemical composition or
morphology in a sample using MRS in the presence of a magnetic
field, such as a non-uniform or spatially varying external magnetic
field. For example, in MRSI the measured dynamic response of other
nuclei in addition to .sup.1H are often used to generate images of
the chemical composition or the morphology of different types of
tissue and the internal anatomy of a patient.
[0044] Additionally, in contrast with existing approaches to MRI or
MRSI that usually provide qualitative or `weighted` measurements of
a limited set of properties, `MRF` should be understood to include
quantitative measurements of the properties of a sample by
acquiring signals representing a dynamic or time-dependent
magnetization or MR trajectory from different materials in a sample
using a pseudorandom pulse sequence. In particular, instead of
using repeated, serial acquisition of data to characterize
individual parameters that are of interest, in MRF signals from
different materials or tissues are often acquired using a
pseudorandom pulse sequence to determine a unique signal or
`fingerprint` (e.g., a time-dependent magnetization or MR
trajectory). The resulting unique fingerprint of the sample is, in
general, a function of multiple material properties under
investigation. For example, MRF can provide high-quality
quantitative maps of: the spin-lattice relaxation time T.sub.1
(which is the time constant associated with the loss of signal
intensity as components of the nuclear-spin magnetization vector
relax to be parallel with the direction of an external magnetic
field), the spin-spin relaxation time T.sub.2 (which is the time
constant associated with broadening of the signal during relaxation
of components of the nuclear-spin magnetization vector
perpendicular to the direction of the external magnetic field),
proton density (and, more generally, the densities of one or more
type of nuclei) and diffusion (such as components in a diffusion
tensor).
[0045] Note that `magnetic-field relaxometry` (such as B.sub.0
relaxometry with the addition of a magnetic-field sweep) may
involve acquiring MR images at different magnetic-field strengths.
These measurements may be performed on the fly or dynamically (as
opposed to performing measurements at a particular magnetic-field
strength and subsequently cycling back to a nominal magnetic-field
strength during readout, i.e., a quasi-static magnetic-field
strength). For example, the measurements may be performed using
un-tuned radio-frequency (RF) coils or a magnetometer so that
measurements at the different magnetic-field strengths can be
performed in significantly less time.
[0046] Moreover, in the discussion that follows `MRE` should be
understood to include measuring the stiffness of a sample using MRI
by sending mechanical waves (such as sheer waves) through a sample,
acquiring images of the propagation of the shear waves, and
processing the images of the shear waves to produce a quantitative
mapping of the sample stiffness (which are sometimes referred to as
`elastograms`) and/or mechanical properties (such as rigidity,
density, tensile strength, etc.).
[0047] Furthermore, MRT should be understood to include measuring
maps of temperature change in a sample using MM.
[0048] In the discussion that follows, note that a biological
lifeform may include a tissue sample from an animal or a person
(i.e., a portion of the animal or the person). For example, the
tissue sample may have been previously removed from the animal or
the person. In some embodiments, the tissue sample is a pathology
sample, such as a biopsy sample. Thus, the tissue sample may be
formalin fixed-paraffin embedded. However, in other embodiments a
biological lifeform may be in the animal or the person (i.e., an
in-vivo sample) and/or the measurement technique involves
whole-body scans. Furthermore, the measurement technique may also
be applied to inanimate (i.e., non-biological) samples of a wide
variety of different materials. In the discussion that follows, the
biological lifeform is a person or an individual, which is used as
an illustrative example. Moreover, while the measurement technique
may be used with a wide variety of non-invasive measurement
techniques, in the discussion that follows MR techniques, and in
particular MRI and MRS, are used as illustrative examples.
[0049] We now describe embodiments of a system. While the pace of
technical innovation in computing and MR software and hardware is
increasing, today MR scans are still performed and interpreted in
an `analog` paradigm. In particular, MR scans are performed with at
best limited context or knowledge about an individual and their
pathologies, and typically are based on a limited set of programs
that are input by a human operator or technician. Similarly, the
resulting MR images are usually read by radiologists based on
visual interpretation with at best limited comparisons with prior
MR images. The disclosed system and measurement technique leverages
a combination of a decreasing cost per clock cycle in the computer
industry and a decreasing cost per Tesla of MR hardware to
facilitate a digital revolution in MR technology and radiology,
with a commensurate impact of accuracy, patient outcomes and
overall cost.
[0050] The disclosed system and measurement technique leverages the
medical histories and prior MR scans of one or more individuals
(which collectively are sometimes referred to as `medical
contexts`) with one or more MR scanners and additional measurement
devices to provide a feedback loop that facilitates targeted,
quantitative MR scans at scale. These targeted scans using one or
more MR techniques may be performed as needed or periodically, and
may be partial scans (such as of regions of interest) or full-body
scans. For example, the one or more MR techniques may be used to
perform, in series or parallel, soft-tissue measurements,
morphological studies, chemical-shift measurements,
magnetization-transfer measurements, MRS, measurements of one or
more types of nuclei, Overhauser measurements, and/or functional
imaging.
[0051] Moreover, a given scan may be dynamically modified when a
potential anomaly is detected to acquire more detailed diagnostic
information. Thus, a region of interest may be scanned using
different resolution (i.e., a different voxel size), a different MR
technique, a different pulse sequence and, more generally, based on
different scanning instructions. In the process, the system may
provide more efficient use of resources, such as reducing scan
times and/or reducing the effort of radiologists and healthcare
providers needed to interpret the scan results. Note that the scans
may be acquired for both healthy individuals and individuals with
pathologies, i.e., symptomatic and asymptomatic individuals.
[0052] Using indexed scans acquired over time and other types data,
the system may build multi-dimensional models of the one or more
individuals that can be used to monitor the individuals' health
and, based on risk factors, may be used to suggest the frequency
and types of diagnostic screenings that should be performed on the
one or more individuals. Note that the risk factors may be
individual-specific and/or may be aggregated risk factors for at
least a subset of the one or more individuals. Moreover, the
multi-dimensional models may include multi-dimensional data, on a
voxel-by-voxel basis, about the volumetric density of particular
chemical signatures, atomic nuclei, etc.
[0053] Thus, the system may intelligently manage automated or
semi-automated analysis of MR scans, as well as the planning and
scheduling of the follow-up scans. For example, the system may
classify detected potential anomalies (such as `known healthy` or
`whitelisted tissue,` `known anomalous` or `blacklisted tissue` or
`unknown` or `greylisted tissue`) either independently or in
conjunction with radiologist feedback. Moreover, the radiologist
feedback may be used to adapt future analysis (such as by modifying
training datasets for one or more supervised-learning techniques),
so that the system is capable of learning and, therefore, can
provide improved analysis and recommendations over time on an
individual and/or a population basis. The feedback may also allow
the system to learn, over time, the differences between different
individuals (such as what may be normal for one individual in their
medical context, as opposed to for another individual in a
different medical context) and to identify new risk factors.
[0054] Note that the system may facilitate these capabilities by,
as needed, capturing, analyzing, storing and subsequently accessing
enormous volumes of data, far more than can be processed by a
single radiologist or even a team of radiologists. Consequently,
the system and the measurement technique may facilitate a paradigm
shift in medical outcomes by `crawling,` at high spatial and
spectral resolution, indexing and searching quantitative MR scans
of the one or more individuals.
[0055] In some embodiments, the initial scan plan includes an MR
scan using a low magnetic field or no magnetic field MR scan (e.g.,
RF only) or a measurement other than MR, such as synthetic aperture
radar (SAR), to scan for ferromagnetic or paramagnetic materials
(e.g., metal plates, pins, shrapnel, other metallic or foreign
bodies) in an individual's body. Alternatively or additionally, the
initial scan may use electron-spin resonance. The initial scan for
paramagnetic materials can improve safety in the system when MR
scanning is used. This may be useful in case an individual's
medical record does not include information about foreign objects,
the foreign objects are new or unknown (e.g., shrapnel fragments
remaining in a wound or in excised tissue), or in the event of an
error. In particular, this `safety scan` can prevent damage or
injury to the individual, and can protect the system from damage.
In addition, the size of any ferromagnetic or paramagnetic material
can be estimated during the initial scan, and a safe magnetic-field
strength for use during the MR scan can be estimated. Conversely,
if the individual does not contain any ferromagnetic of
paramagnetic materials, one or more higher magnetic-field strengths
can be used during one or more subsequent MR scans.
[0056] Moreover, in some embodiments the measurement technique uses
so-called `breadth-first indexing` as a form of compressed sensing.
In particular, the system may spend more time scanning and modeling
interesting or dynamic parts of an individual, and may avoid
spending time on parts that are not changing rapidly. Note that
`interesting` regions may be determined based on information
gathered in real-time and/or based on historical information about
the individual being scanned or other individuals. The
breadth-first indexing may employ inference or inductive
techniques, such as oversampling and/or changing the voxel size in
different regions in the body based on an estimated abundance of
various chemical species or types of nuclei (which may be
determined using chemical shifts or MRS). As noted previously and
described further below, the scan plan in such breath-first
indexing may be dynamically updated or modified if a potential
anomaly is detected.
[0057] In the discussion that follows, a scan plan can include a
scan of some or all of an individual's body, as well as a reason or
a goal of the scan. For example, a scan plan may indicate different
organs, bones, joints, blood vessels, tendons, tissues, tumors, or
other areas of interest in an individual's body. The scan plan may
specify, directly or indirectly, scanning instructions for an MR
scanner that performs the scan. In some embodiments, the scan plan
includes or specifies one or more MR techniques and/or one or more
pulse sequences. Alternatively, the one or more MR techniques
and/or the one or more pulse sequences may be included or specified
in the scanning instructions. As described further below, the
scanning instructions may include registration of an individual, so
that quantitative comparisons can be made with previous MR scans on
the same or another occasion. Thus, at runtime, the areas of
interest in the scan may be mapped to 3D spatial coordinates based
on a registration scan.
[0058] The scan plan, as well as the related scanning instructions
(such as the voxel size, one or more spectra, one or more types of
nuclei, pulse sequences, etc.), may be determined based on a wide
variety of information and data, including: instructions from a
physician, medical lab test results (e.g., a blood test,
urine-sample testing, biopsies, etc.), an individual's medical
history, the individual's family history, comparisons against
previous MR scan records, analysis of MR signals acquired in a
current scan, and/or other inputs. In some embodiments, the MR scan
plan is determined based on risk inputs, such as inputs used to
determine the individual's risk to pathologies that are included in
a pathology knowledge base. The risk inputs can include: age,
gender, current height, historical heights, current weight,
historical weights, current blood pressure, historical blood
pressures, medical history, family medical history, genetic or
genomic information for the individual (such as sequencing,
next-generation sequencing, RNA sequencing, epigenetic information,
etc.), genetic or genomic information of the individual's family,
current symptoms, previously acquired MR signals or images,
quantitative tensor field maps, medical images, previous blood or
lab tests, previous microbiome analysis, previous urine analysis,
previous stool analysis, the individual's temperature,
thermal-imaging readings, optical images (e.g., of the individual's
eyes, ears, throat, nose, etc.), body impedance, a hydration level
of the individual, a diet of the individual, previous surgeries,
previous hospital stays, and/or additional information (such as
biopsies, treatments, medications currently being taken, allergies,
etc.).
[0059] Based on scanning instructions that are determined from an
initial scan plan (such as using predefined or predetermined pulse
sequences for particular at-risk pathologies), the system may
measure and store for future use MR signals, such as MR signals
associated with a 3D slice through the individual. In general, the
MR measurements or scans may acquire 2D or 3D information. In some
embodiments, the MR measurements include animations of the
individual's body or a portion of their body over time, e.g., over
weeks, months, years, or shorter timescales, such as during a
surgical procedure.
[0060] As noted previously, during the measurements the system may
perform a registration scan, which may include a fast morphological
scan to register, segment, and model a body in 3D space, and to
help calibrate noise-cancelation techniques, such as those based on
motion of the individual. For example, the system may include
optical and thermal sensors, as well as pulse monitoring, to
measure motion of the individual associated with their heartbeat
and respiration. Note that a scan can be interrupted to re-run a
registration scan to make sure an individual has not shifted or
moved. Alternatively or additionally, the measured MR signals
during a scan may be used to track and correct the motion of the
individual. This correction may be performed during a scan (e.g.,
by aggregating MR signals associated with a voxel at a particular
3D position) and/or subsequently when the MR signals are
analyzed.
[0061] In some embodiments (such as during MRI), the system may
determine segments of the individual's body. This segmentation may
be based, at least in part, on a comparison with segments
determined in one or more previous scans. Alternatively or
additionally, the measurements may include a segmentation scan that
provides sufficient information for a segmentation technique to
correctly segment at least a portion of the body of the individual
being imaged.
[0062] Then, the system may analyze the MR signals. This analysis
may involve alignment of voxels based on registration of the 3D
positions of the voxels in the individual in the current scan with
those in one or more previous scan(s) for the same and/or other
individuals. Alternatively or additionally, the system may resample
and/or interpolate measured or simulated MR signals from the 3D
positions of the voxels in the previous scan(s) to the 3D positions
of the voxels in the current scan.
[0063] During the analysis, the system may compare current and the
previous MR signals. Note that the comparison may be facilitated
using a look-up table. For example, the system may MR signals from
a voxel with a value in a look-up table that is based on simulated
MR signals associated with a previous scan. In this way, the system
can compare metabolic chemical signatures between adjacent voxels
in an MRS scan to detect a potential anomaly or can perform
comparisons to MR signals that are a composite of two or more
individual's bodies. Thus, the comparison may be performed on a
voxel-by-voxel basis.
[0064] In some embodiments, the system performs the analysis by
computing an invariant MR signature based on MR signals measured in
a current scan and/or computes simulated MR signals based on one or
more previously determined invariant MR signatures.
[0065] Based on the comparison, the system may classify a voxel as:
low risk, high risk or unknown risk. For example, a voxel may be
classified as indicative of: early-stage cancer, late-stage cancer,
or an unknown-stage cancer. In particular, the system may perform
automatic quantitative processing of MR signals from the individual
voxels based on a library of baseline tissue characterizations or
templates. In this way, quantitative MR measurements (such as MRF)
based on a scan plan can be used to quantify the health of:
particular organs (such as scanning the liver of the individual for
cancer), performing assays of blood, detecting known-good and
known-bad quantitative signatures of specific tissues (e.g., skin,
heart, liver, muscle, bone, etc.), performing post-biopsy analysis,
another type of evaluation, etc.
[0066] The resulting classifications (including unknown
classifications) may be provided to a radiologist (such as via a
graphical user interface that is displayed on a display). In
particular, the radiologist may provide a classification,
identification feedback or verification feedback. The information
from the radiologist may be used to update the analysis (such as
one or more supervised-learning models, the look-up table and/or
the associated classifications).
[0067] When a potential anomaly is detected, the system may
dynamically revise or modify the scan plan (and, thus, the scanning
instructions) based on the detected potential anomaly, as well as
possibly one or more of the factors mentioned previously that were
used to determine the initial scan plan. For example, the system
may change the voxel size, a type of nuclei, the MR technique (such
as switching from MRI to MRS), etc. based on the detected potential
anomaly. The modified scan plan may include a region that includes
or that is around the detected potential anomaly. Thus, the size of
the region may be determined based on a size of the detected
potential anomaly. Alternatively or additionally, the region in the
modified scan plan may be determined based on a location or segment
in the individual's body where the potential anomaly is
located.
[0068] Next, the system may perform additional MR measurements,
which are then analyzed and stored for future use. Note that this
additional scan may occur after completion of the first or initial
scan of the individual. For example, the modified scanning
instructions may be queued for execution after the first scan is
completed. Alternatively, when the potential anomaly is detected,
the first scan may be stopped (i.e., when it is only partially
completed) and the partial MR signals may be stored and/or provided
to the system. In some embodiments, the system stops the first scan
by providing an interrupt to the MR scanner. Then, after the second
or the additional scan is completed, the MR scanner may complete
the first scan, and the remainder of the MR signals may be stored
and/or provided to the system. In order to complete the interrupted
or stopped first scan, the MR scanner may save or store information
that specifies the current position when it stopped, as well as the
scanning context (such as the MR measurement being performed). This
positioning and scanning context information may be used by the MR
scanner when the first scan is resumed.
[0069] After completing the first and/or the second MR scan (or any
additional related scans), as well as the associated analysis, the
system may determine a recommended time for a follow up scan of the
individual based on any detected anomalies (and, more generally,
the results of the current MR scan(s) and/or one or more previous
MR scans) and/or any of the aforementioned factors that were used
to determine the scan plan(s). Moreover, the system may determine a
future scan plan for the individual or another individual based on
the results of the current MR scan(s) and/or comparisons of the
current MR scan(s) with one or more previous MR scans. This
capability may allow the system to facilitate monitoring of one or
more individuals over time or longitudinally. Furthermore, this
approach may allow the feedback from even a single radiologist to
impact the future scan plans of one or more individuals.
[0070] As described further below, when determining a scan plan
and/or analyzing measured or acquired MR signals the system may
access a large data structure or knowledge base of invariant MR
signatures from multiple individuals (which is sometimes referred
to as a `biovault`), which may facilitate quantitative comparisons
and analysis of MR scans. The biovault may include: invariant MR
signatures, additional information and/or identifiers of
individuals in the data structure (such as unique identifiers for
the individuals). Furthermore, the additional information may
include diagnostic information or metadata associated with previous
measurements on the individuals or tissue samples associated with
the individuals, including: weight, size/dimensions, one or more
optical images, one or more infrared images, impedance/hydration
measurements, data associated with one or more additional MR
techniques, demographic information, family histories and/or
medical histories. Note that the biovault may include information
for symptomatic and/or asymptomatic individuals. (Therefore, the
individuals may not solely be healthy or unhealthy. For example, a
particular invariant MR signature may be healthy in certain medical
contexts, such as for a particular person, but may be unhealthy in
another medical context.) Thus, the biovault can be used to
characterize healthy tissue, as well as disease or pathology.
[0071] FIG. 1 presents a block diagram illustrating an example of a
system 100. This system includes: an MR scanner 110 and computer
system 114. As described further below with reference to FIG. 8,
computer system 114 may include: a networking subsystem (such as an
interface circuit 116), a processing subsystem (such as a processor
118), and a storage subsystem (such as memory 120). During
operation of system 100, a technician or an MR operator can scan or
read in information about an individual 112 using
sample-information reader (SIR) 122 to extract information (such as
an identifier, which may be a unique identifier) from a label
associated with individual 112 (who is used as an illustrative
example of a biological lifeform in the discussion that follows).
For example, sample-information reader 122 may acquire an image of
the label, and the information may be extracted using an optical
character recognition technique. More generally, note that
sample-information reader 122 may include: a laser imaging system,
an optical imaging system (such as a CCD or CMOS imaging sensor, or
an optical camera), an infrared imaging system, a barcode scanner,
an RFID reader, a QR code reader, a near-field communication
system, and/or a wireless communication system.
[0072] Alternatively, the technician or the MR operator may input
information about individual 112 via a user interface associated
with computer system 114. Note that the extracted and/or input
information may include: the unique identifier of individual 112
(such as a subject or patient identifier), an age, a gender, an
organ or a tissue type being studied, a date of the MR scan, a
doctor or practitioner treating or associated with individual 112,
the time and place of the MR scan, a diagnosis (if available),
etc.
[0073] Then, the technician or the MR operator can place individual
112 in MR scanner 110, and can initiate the MR scans (which may
involve MRF, MRT, MRE, MRS, magnetic-field relaxometry, etc.)
and/or other measurements, e.g., by pushing a physical button or
activating a virtual icon in a user interface associated with
computer system 114. Note that the same individuals (and, more
generally, the same tissue sample or material) can have different
MR signals (such as different signal intensities and/or
frequencies) in different datasets that are measured in the same MR
scanner or in different MR scanners. In general, such
measurement-to-measurement variation depends on many factors,
including: the particular instance of MR scanner 110, a type or
model of MR scanner 110, a set-up of MR scanner 110, the scanning
instructions (such as the magnetic-field strengths, magnetic
gradients, voxel sizes, the pulse sequences that are applied to
individual 112, the MR techniques, the regions of interest in
individual 112, one or more voxel sizes and/or the types of nuclei
or molecules), a detector in MR scanner 110, and/or one or more
signal-processing techniques. For example, the one or more
signal-processing techniques may include: gradient-echo imaging,
multi-slice imaging, volume imaging, oblique imaging, spin-echo
imaging, inversion recovery imaging, chemical contrast agent
imaging, fat suppression imaging using spin-echo imaging with
saturation pulses before taking regular images, etc.
[0074] These challenges are addressed in system 100 in the
measurement technique by performing MR scans and comparing the
associated MR signals with simulated MR signals based on one or
more previously determined invariant MR signatures of at least
individual 112, which are independent of (or has significantly
reduced sensitivity to) variations in the magnetic-field strength
(and, thus, magnetic-field inhomogeneity). Alternatively, the MR
signals acquired in the MR scans may be used to determine an
invariant MR signature, which may be compared to one or more
previously determined invariant MR signatures.
[0075] The one or more invariant MR signatures may include the
information found in or corresponding to the information in an MR
fingerprint at least of individual 112 (such as high-quality
quantitative maps of T.sub.1, T.sub.2, nuclei density, diffusion,
velocity/flow, temperature, off-resonance frequency, and magnetic
susceptibility). Moreover, the one or more invariant MR signatures
may be corrected for measurement-to-measurement variation
(including variation that occurs from one MR scanner to another).
Alternatively, the one or more invariant MR signatures may include
information that corrects for measurement-to-measurement variation
and/or that allows a version of an MR image, an MR spectra, an MR
fingerprint, etc. to be generated for particular measurement
conditions, such as: a particular MR scanner, a particular model of
the MR scanner, scanning instructions, a particular detector, etc.
Thus, in conjunction with characteristics of a particular MR
scanner (such as the model of this particular MR scanner, the
scanning instructions, the detector, noise characteristics of the
particular MR scanner, magnetic-field inhomogeneity in the
particular MR scanner), the one or more invariant MR signatures may
be used to generate or calculate a version of an MR image, an MR
spectra, an MR fingerprint, etc. as if it were measured by the
particular MR scanner. Note that the noise characteristics of the
particular MR scanner may depend on the pulse sequence used.
[0076] In some embodiments, an invariant MR signature includes
parameters in an MR model of voxels in at least individual 112.
Because each voxel in the MR model may include multi-dimensional
data on the volumetric density of certain chemical signatures and
atomic nuclei, the invariant MR signature of individual 112 may be
based on an awareness of one or more regions of individual 112. For
example, the voxel size in the MR model may depend on an anatomical
location in individual 112.
[0077] Moreover, system 100 may use the information in the
biovault, the MR signals acquired in an initial scan of individual
112 and/or one or more detected potential anomalies to further
optimize the scan plan and, thus, scanning instructions (and, more
generally, the conditions during the MR measurements) when
collecting additional MR signals from individual 112. For example,
the extracted and/or input information about individual 112, as
well as additional stored information in memory 120 that is
accessed based on the unique identifier (such as a medical record
or medical history that is linked or queried based on the unique
identifier), may be used by computer system 114 to update the
scanning instructions (such as different pulse sequences and/or
different magnetic-field strengths, e.g., a range of magnetic-field
strengths, including 0 T, 6.5 mT, 1.5 T, 3 T, 4.7 T, 9.4 T, and/or
15 T, the MR techniques, the regions of interest in individual 112,
the voxel sizes and/or the types of nuclei), the other measurements
to perform and, more generally, a scan or analysis plan. In
general, the scanning instructions may specify more than a single
value of the magnetic-field strength. For example, the scanning
instructions may provide or specify a function that describes how
the magnetic field will change over time and in space, or multiple
functions that specify a `surface` that can be used to determine
the invariant MR signature of individual 112. As described further
below with reference to FIG. 2, in some embodiments the magnetic
field is physically and/or virtually manipulated to achieve the
specified surface. In particular, the magnetic field may be rotated
as a function of time, or in embodiments with physically separate
ring magnets that generate the magnetic field, the magnetic field
may be changed by: changing the physical distance between the ring
magnets, changing the orientation of one ring magnet with respect
to the other ring magnet, moving a ring magnet along the z axis,
etc.
[0078] Moreover, as described further below, note that the other
measurements may include: impedance measurements, optical imaging,
scanning of dimensions of individual 112, weighing individual 112
and/or other tests that may be included in the measurement
technique. For example, a gel-covered table in MR scanner 110 can
be used to measure an impedance of individual 112 and/or a weight
of individual 112. In some embodiments the other measurements probe
individual 112 non-destructively (e.g., using electromagnetic or
mechanical waves). However, in other embodiments the measurement
technique includes integrated therapeutics, such as: proton beam
therapy, radiation therapy, magnetically guided nano particles,
etc.
[0079] In addition, predetermined characterization of MR scanner
110 may be used to determine the scanning instructions.
Alternatively, if MR scanner 110 has not already been
characterized, system 100 may characterize and store
characteristics of MR scanner 110 prior to calculating simulated MR
signals or determining the invariant MR signature, so that the
characteristic of MR scanner 110 can be used during the measurement
technique, such as to determine the scanning instructions. For
example, during operation, computer system 114 may characterize MR
scanner 110 based on scans of a phantom.
[0080] Note that the predetermined characterization of MR scanner
110 may include a mapping or determination of the inhomogeneity of
the magnetic field of MR scanner 110 (because the inhomogeneity may
depend on the magnetic-field strength, measurements may be
performed at different magnetic-field strengths). The predetermined
characterization may also include environmental, geographical
and/or other parameters. For example, RF pulses generated by a
pulse generator in system 100 may vary from one MR scanner to
another, and may vary as a function of time because the performance
of components may depend on parameters such as: the load, the
temperature, the MR coil configuration, amplifiers, humidity,
magnetic storms and/or geolocation. Consequently, in addition to MR
signals, the RF pulses (and/or the inhomogeneity in the RF pulses)
may be measured, e.g., using a signal splitter between an RF pulse
generator and an RF (transmission) coil in MR scanner 110. In some
embodiments, the magnetic field produced by the RF coil is measured
using a test coil. Note that, because a specific pulse sequence may
correspond to a specific voxel size, different pulse sequences
corresponding to different voxel sizes may be used when
characterizing MR scanner 110 and/or when determining the scanning
instructions.
[0081] As described further below with reference to FIG. 3, the
measurements and recorded signals associated with MR scanner 110
may be used to generate an MR model of MR scanner 110 that
accurately predicts MR signal evolution or response for a phantom
having known properties over a range of parameters (T.sub.1,
T.sub.2, proton density, off-resonances, environment, location,
temperature, pulse sequences, etc.) using the Bloch equations, full
Liouvillian computations or another simulation technique. In this
way, the MR model may characterize MR scanner 110.
[0082] The predetermined characterization of MR scanner 110 can be
used to transform a generic invariant MR signature into a
machine-specific invariant MR signature associated with a
particular MR scanner, such as MR scanner 110. In conjunction with
the magnetic field and the pulse sequence, the machine-specific
invariant MR signature may be used to predict or calculate
simulated MR signals during an arbitrary MR scan in the particular
MR scanner. Similarly, predetermined characterizations of different
MR scanners can be used to convert from one machine-specific
invariant MR signature to another.
[0083] In some embodiments, the predetermined characterization of
MR scanner 110 includes measured ambient noise from electronics in
or associated with MR scanner 110. During subsequent MR scans or
simulations, digital filters may use the measured noise (or
statistical parameters that describe the measured noise) to improve
the quality of measured MR signals and/or calculated MR models.
Moreover, the various measurements may be synchronized with an
external reference clock or to a biological time period (such as a
respiration period, a heart-beat period, a fundamental period for
body motion, etc.) to enable subsequent synchronous averaging or
additional signal processing.
[0084] Moreover, during the measurement technique, computer system
114 may repeatedly perform MR scans of different materials (such as
different types nuclei) in individual 112 using MR scanner 110
based on instances of the scanning instructions that are received
via network 130. Note that the MR scans of the different materials
may be pseudorandomly acquired. For example, an MR scan of a
particular material in individual 112 may be selected based on a
random or a pseudorandom number provided by a circuit or
software-implemented random or a pseudorandom number generator in
computer system 114. Alternatively, the different materials in
individual 112 may be systematically scanned for each instance of
the scanning instructions.
[0085] Furthermore, the MR signals acquired or captured during a
particular MR scan may be used to modify or adapt an MR model of
voxels in individual 112. For example, as noted previously and as
described further below with reference to FIG. 3, computer system
114 may determine the MR model (such as parameters in the MR model)
based on differences (or a difference vector) between MR signals
associated with the voxels in one or more MR scans and simulated or
calculated MR signals (which may be generated using the MR model,
an instance of the scanning instructions and optionally the
characteristics of MR scanner 110). Note that the difference vector
may be weighted based on a priori computed information to reduce
the error, e.g., to obtain the smallest difference vector or the
smallest difference vector measured across a set of weighted
simulated MR signals (which may be precomputed). In some
embodiments, the difference vector is determined using a dot
product or inner product of one or more MR signals and simulated MR
signals (which are each associated with or corrected to a common
magnetic-field strength), cosine similarity between one or more MR
signals and simulated MR signals, spectral analysis, and/or another
comparison technique.
[0086] Then, based on the remaining differences (or the remaining
difference vector) and/or one or more detected potential anomalies,
the scanning instructions may be modified, i.e., a new instance of
the scanning instructions (including one or more magnetic-field
strengths and one or more pulse sequence(s) that will be applied to
individual 112, the MR technique, the regions of interest in
individual 112, the voxel sizes and/or the types of nuclei) may be
determined. These operations may be iteratively repeated until a
convergence criterion is achieved. For example, the convergence
criterion may include that the difference between the MR signals
and the simulated MR signals is less than a predefined value (such
as 0.1, 1, 3, 5 or 10%) and/or that the changes to the scanning
instructions are less than the predefined value. Furthermore, the
convergence criterion may include completion of the scan plan.
[0087] As noted previously, these capabilities of the system 100
may allow scans to be performed as needed, after a time interval or
periodically on an individual, so that the biovault can amass
information and knowledge about the individual's (as well as other
individuals) body and health. This information and knowledge can be
used tailor or target scan plans based on the individual's needs,
such as based on changes over time in their body.
[0088] We now further describe operations in the measurement
technique in more detail. FIG. 2 presents a block diagram of an
example of MR scanner 110. This MR scanner may include a magnet
210, magnetic shielding 212, a biological lifeform holder (BLH)
214, a biological lifeform holder articulator (BLHA) 216, a
magnetic-gradient pulse generator (MGPG) 218, a magnetic-gradient
amplifier (MGA) 220, magnetic-gradient coils 222, an RE pulse
generator (RFPG) 226, an RF source (RFS) 224, RF amplifier (RFA)
228, RF coils 230, an RF receive amplifier (RFRA) 232, an RF
detector (RFD) 234, a digitizer 236 (such as an analog-to-digital
converter), an environmental conditioner 242 and an interface
circuit 244. (Note that mechanical and electrical connections to
environmental conditioner 242 and interface circuit 244 are not
shown in FIG. 2.) At least some of these components may be coupled,
via interface circuit 244, network 130 (FIG. 1) and interface
circuit 116 (FIG. 1), to computer system 114, which may control
operation of MR scanner 110. The components in MR scanner 110 are
described briefly below.
[0089] Note that MR scanner 110 may be a closed-bore or an
open-bore system. In particular, magnet 210 (illustrated in a
cross-sectional view in FIG. 2 by portions of magnet 210-1 and
210-2) may be closed bore or open bore. For example, a bore
diameter 238 of magnet 210 may be between 1 and 10 cm or between 5
and 30 cm. An open-bore system may generate a magnetic field using
two plates separated by a gap, and individual 112 may be exposed to
(and nuclei in individual 112 may be polarized by) the magnetic
field between the plates. Alternatively, a closed-bore system may
have a toroidal shaped magnet 210, individual 112 may be moved
through a hole in the center of the toroid (thus, using a strong
field or high field to polarize nuclei in individual 112).
Moreover, the orientation of magnet 210 may be horizontal (which is
sometimes referred to as `horizontal bore`) so that individual 112
moves horizontally through the magnetic field, but can also be
vertically oriented. In general, MR scanner 110 may scan individual
112 in various positions, including at different angles,
orientations and perspectives (e.g., by adjusting biological
lifeform holder articulator 216). (Thus, when MR scans are
performed on individuals or animals, MR scanner 110 may allow
measurements to be made while an individual is standing, sitting,
laying down, positioned on their side or even in motion, such as
walking on a treadmill.) Note that embodiments with a smaller bore
diameter 238 may allow MR scanner 110 to be portable.
[0090] Depending on the MR technique, the magnetic-field strength
B.sub.0 of magnet 210 may be low field (e.g., an electromagnet
having a peak magnetic-field strength that is less than 0.1 T, such
as a magnetic-field strength as low as 0.001 T or even 0 T), a
strong field (e.g., a ferro-magnet having a peak magnetic-field
strength of around 0.5 T) or high field (e.g., a superconducting
magnet having a peak magnetic-field strength greater than around
0.5 T). In general, a wide variety of magnets and magnetic
configurations may be used. In embodiments with a superconductor,
magnet 210 may be cooled using a cryogenic fluid, such as liquid
helium or liquid helium in a surrounding dewar filled with liquid
nitrogen or that is refrigerated. However, in other embodiments
magnet 210 operates at or near room temperature. Furthermore,
magnet 210 may be modular, such as a set of superconducting rings
that each have a peak magnetic-field strength of 0.5 T and that can
be added, removed or moved to create different magnetic-field
magnitudes and configurations.
[0091] Magnet 210 may produce magnetic fields that can be changed
physically and/or virtually (via gradient fields and/or pulse
sequences). This capability may allow slow rotation of the main
external magnetic field, so that MRS can be performed at low
magnetic-fields strengths. This additional degree of freedom may
provide more ways to perturb the magnetic moments in individual 112
to obtain information that can reduce the complexity of the
invariant MR signature calculations. Note that moving or changing
the orientation of magnet 210 may involve: moving pairs of ring
magnets closer or further away on the z axis as part of a scan
plan; rotating magnet 210 relative to the volume of space being
indexed; changing the orientation/alignment of magnet 210 with
respect to the z axis of the volume being indexed, etc. Moreover,
`physically` can mean physical movement of magnet 210, while
`virtually` may indicate that gradient fields and/or pulse
sequences (such as a so-called `spin-lock technique`) are used to
achieve the same result without physically changing the orientation
of magnet 210. In general, these techniques may be used
independently of each other or two or more of the techniques may be
used in conjunction with each other.
[0092] Magnet 210 may also be used to (intentionally) dynamically
vary the magnetic-field inhomogeneity. For example, by physically
rotating a shim coil and/or by applying particular pulse sequences,
the magnetic-field inhomogeneity may be modified. Moreover, by
introducing specific kinds of magnetic-field inhomogeneity at
different points in space, MR scanner 110 can differentiate certain
kinds of tissue that are in close proximity.
[0093] Magnetic shielding 212 may include steel plates or metal
sheets of silicon steel. This magnetic shielding may be placed all
around a room, fully covering walls, floors and ceilings, in order
to attenuate the magnetic-field strength outside the room to below
5 Gauss (or 0.5 mT). Moreover, special doors and doorframe seals
may be used to further reduce the magnetic field that `leaks` out
of the room. Furthermore, magnet 210 may include shielding (such as
a second set of superconducting windings with an opposite current
flow than the main superconducting windings) in order to reduce the
fringe magnetic field. For example, the magnetic-field strength may
be 0.5 mT at a distance of four meters from magnet 210. This
configuration may reduce the amount of magnetic shielding 212 or
may eliminate the need for magnetic shielding 212 entirely.
[0094] In some embodiments, magnetic shielding 212 may provide a
chamber 240 (defined by a surface of magnetic shielding 212), and
this chamber may be optionally sealed so that at least a portion of
individual 112 or a tissue sample being measured is at less than
atmospheric pressure (i.e., a vacuum chamber) or may contain an
inert gas (such as xenon) that can be pre-polarized to improve the
MR imaging quality. (More generally, a solid, liquid or gas
contrast agent may be used to improve the MR imaging quality.) In
particular, environmental conditioner 242, such as a gas valve and
a vacuum pump that are controlled by computer system 114, may be
used to reduce the pressure in chamber 240. Alternatively,
environmental conditioner 242 may include the gas valve and a gas
tank that selectively allow (under control of computer system 114)
the inert gas to flow into chamber 240. However, in other
embodiments chamber 240 is defined by or provided by a surface of
biological lifeform holder 214.
[0095] Note that magnetic-gradient pulse generator 218 may provide
gradient pulses. These gradient pulses may be amplified by
magnetic-gradient amplifier 220 to a level suitable for driving
magnetic-gradient coils 222. Note that magnetic-gradient pulse
generator 218 and magnetic-gradient amplifier 220 may be controlled
by computer system 114 via an interface circuit 116 (FIG. 1),
network 130 (FIG. 1) and interface circuit 244. For example,
computer system 114 may specify the types and shapes of magnetic
pulses provided by magnetic-gradient pulse generator 218, and may
specify the amplification or gain of magnetic-gradient amplifier
220.
[0096] Moreover, magnetic-gradient coils 222 may produce the shape
and amplitude of the gradient magnetic field along the x, y and z
axes (in a right-handed Cartesian coordinate system).
Magnetic-gradient coils 222 typically operate at room temperature
and may produce spatial gradients in the magnetic field B.sub.0.
For example, in a horizontal bore system, a gradient in the
magnetic field B.sub.0 along the z-axis or direction (i.e.,
parallel to a symmetry axis of the bore of magnet 210) may be
achieved using an anti-Helmholtz coil, with current in each coil
adding to or subtracting from the magnetic field B.sub.0 to achieve
the gradient. Furthermore, gradients along the x and y-axes may be
generated or created using a pair coils having a `FIG. 8` shape
(which create gradients along their respective axes).
[0097] In some embodiments, magnetic-gradient coils 222 have
gradients of 100 mT/m and have fast switching times (or slew rates)
of 150 T/m/s, which may enable a slice thickness of 0.7 mm and a
voxel resolution of 0.1 mm in 3D imaging. However, by using high
frequencies (such as frequencies above approximately 100 kHz), slew
rates higher than the current U.S. slew-rate limit of 200 T/m/s may
be used. If magnet 210 produces a larger magnetic-field strength
(such as 7 T), an isometric voxel resolution of 60 .mu.m may be
achieved.
[0098] Furthermore, RF pulse generator 226 may generate RF pulses
based on carrier waves output by RF source 224 (such as sinewaves
or RF pulses having desired fundamental frequencies based on a
target type of nuclei and magnetic-field strength B.sub.0), and RF
amplifier 228 may increase the power of the RF pulses to be strong
enough to drive RF coils 230 (e.g., increasing the power from
milliWatts to kiloWatts). RF coils 230 may create a magnetic field
B.sub.1 that rotates the net magnetization of type of nuclei in
individual 112 based on the pulse sequence. Note that RF pulse
generator 226, RF source 224 and RF amplifier 228 may be controlled
by computer system 114 via interface circuit 116 (FIG. 1), network
130 (FIG. 1) and interface circuit 244. For example, computer
system 114 may specify the type or shape of pulse(s) output by RF
pulse generator 226, the frequencies in the carrier frequencies or
pulses provided by RF source 224 and/or the amplification or gain
of RF amplifier 228.
[0099] In some embodiments, RF pulse generator 226 shapes the
carrier waves or RF pulses into apodized sinc pulses, which may
smooth discontinuities that can adversely affect the measurements
and/or subsequent signal processing (such as a Fourier transform).
Apodized sinc pulses may excite the spin states of the nuclei, and
these excited spin states may decay and release a pulse of RF
energy that is captured during acquisition. In general, a wide
variety of pulse sequences may be used during the measurement
technique. For example, the pulse sequence may include or may be
associated with MR techniques such as: turbo field echo (TFE), fast
field echo (FFE), susceptibility weighted imaging (SWE), short tau
inversion recovery (STIR) or short T.sub.1 inversion recovery (a
type of suppression technique for fatty tissue with an inversion
time TI equal to T.sub.1ln(2) so that the MR signal of fat is
zero), turbo spin echo (TSE), fast low angle shot or FLASH (a type
of spin-echo sequence in which larger tip angles provide more
T.sub.1-weighted images and smaller tip angles provide more
T.sub.2-weighted images), volumetric interpolated brain examination
(VIBE), magnetic pulse rapid gradient echo (MP RAGE), fluid
attenuation inverted recovery (FLAIR), a parallel imaging technique
such as sensitivity encoding (SENSE), or another pulse sequence.
Note that SENSE may involve: generating coil sensitivity maps,
acquiring partial k-space MR data, reconstructing partial field of
view images from each of RF coils 230, and combining the partial
field of view images using matrix inversion. Moreover, the pulse
sequence may include or may be associated with second and third
generation parallel imaging techniques, such as GRAPPA, Auto-Smash
or VD-SMASH, which are imaging techniques that speed up MRI pulse
sequences using k-space undersampling, and the acquisition of
additional lines provides a form of calibration because the
coefficients of MR signals across RF coils 230 can be determined
from the measurements. Furthermore, the pulse sequence(s) may be
designed or selected to be independent of the hardware or MR
scanner. For example, a pulse sequence may be designed or selected
to cancel noise and amplify specific parameters of interest (which
is sometimes referred to as `quantum pumping`). (These pulse
sequences may be used in NMR or MRI to quantify certain parameters
in a machine-independent manner). As described below, quantum
pumping may be used an alternative to pseudorandom pulse
sequences.
[0100] Thus, in general, the pulse sequences may include: existing
pulse sequences (when accurate measurements and simulations of the
properties of the MR scanner can be obtained so that invariant MR
signatures can be determined); pseudorandom pulse sequences (which
may also involve accurate measurement and simulation of noise, but
the pseudorandom nature may help to create more unique Bloch
trajectories at each point in space); and/or quantum pumping (which
may, at least in part, cancel out MR scanner-dependent noise, and
thus, may simplify or reduce the required accuracy of the
simulations used to determine the invariant MR signatures).
[0101] RF coils 230 also may detect the transverse magnetization as
it precesses in the xy plane. In general, a given one of RF coils
230 may be transmit only, receive only or can transmit and receive
RF signals. Moreover, RF coils 230 may be oriented such that the
magnetic field B.sub.1 is perpendicular to the magnetic field
B.sub.0. Furthermore, RF coils 230 may be tuned to the Larmor
frequency (e.g., the resonant frequency of a type of nuclei being
imaged or measured at the magnetic field B.sub.0), e.g., by
adjusting a capacitor or an inductor, or changing its capacitance
or inductance (such as by using matching and tuning capacitors).
Note that RF coils 230 may include: an Alderman-Grant coil, a bird
cage (which may be used for volume measurements), a butterfly coil,
a dome resonator, a gradiometer, an implantable coil, an inside
out/Schlumberger coil, an intravascular coil, a ladder coil, a Litz
coil, a loop-gap resonator coil, a loop-stick coil, a meanderline
coil, a mouse coil, a multi-turn solenoid coil, a phased-array
coil, a phased-array volume coil, a ribbonator coil, a saddle coil,
a scroll coil, a single-turn solenoid coil (which may be used for
extremity measurements), a spiral coil, a surface coil (which may
be used for receiving body or volume signals because they have a
good signal-to-noise ratio for tissues and samples adjacent to the
coil), a multi-nuclear surface coil, a diffusion-tensor-imaging
surface coil, a superconducting coil, a transmission-line coil, a
truncated-spiral coil, a 3-axis coil, and/or a wide-band RF coil
(which may be used to simultaneously excite multiple spectra). Note
that coils with additional density can be designed to focus on
regions of particular interest, such as: the brain, the abdomen,
the chest, the reproductive, organs, spine, a joint (e.g., the
neck, a shoulder, a knee, an elbow, a wrist, etc.), hands or feet.
Moreover, the one or more of RF coils 230 may be full-body coils
that are designed to capture the full body.
[0102] In some embodiments, one or more of RF coils 230 includes a
thermal imaging sensor, which can include a forward looking
infrared (FUR) sensor. (This may allow thermal imaging and MRI of,
e.g., breast tissue.) Note that one or more sensors (such as the
one or more of RF coils 230) in MR scanner 200 can be attached
modularly (e.g., snapped together in concentric shells, snapped on
additions, assembled with interlocking interfaces, etc.) and can
communicate with each other via wireless or wired
communication.
[0103] Furthermore, the one or more of RF coils 230 may be included
in form-fitting elastic fabric that resembles football pads or suit
of armor, and the size can be adjusted based on the size of
individual 112. Additional RF coils can be included in hats,
helmets, long-sleeve shirts, pants, gloves, socks, legwarmers,
tights, jackets, vests, breeches, and/or other clothing items. For
example, a measurement-equipped suit may include a soft wearable
set of RF coils that is worn by individual 112, and then individual
112 can also be enclosed in a more rigid suit, such as a clamshell
design. Note that the soft, wearable clothing suit may have one or
more integrated ultrasonic generators attached to some or all parts
of the body and/or integrated electrocardiogram sensors, and the
harder outer shell may include integrated optical and thermal
sensors. In some embodiments, a head coil includes: a mirror, a
prism, a fiber-optic cable, a holographic display, a retinal
projector, a projection screen, a stereo-projection screen, and/or
another type of display for presenting visual information.
[0104] Moreover, in some embodiments surface coils that can be
controlled by software on computer system 114 that executes the
scan plan allow certain modalities or MR techniques to be turned on
and off in real-time as the analysis of individual 112 progresses
(such as during a second MR scan in response to detection of a
potential anomaly, which is sometimes referred to as a `drill down`
protocol scan) For example, this approach may allow MRE to be
performed on an anomaly, or a thermal image to be acquired of
individual 112 or the surrounding region. Thus, if a potentially
anomaly is detected in the individual's chest, the system may
decide to send an ultrasonic wave through their chest during MRE of
the potential anomaly and/or the surrounding region. In these
embodiments, RF coils 230 can be constructed to include multiple
sensors and data-collection equipment to facilitate specialized
anomaly detection. Thus, RF coils 230 may be optimized for parallel
collection of data using: MRF, MRT, MRS, MRE, multi-nuclear imaging
of two or more nuclei (such as .sup.1H, .sup.23Na, .sup.31P,
.sup.13C, .sup.19F, .sup.39K, .sup.43Ca, etc.), diffusion-tensor
imaging, N-channel scanning, magnetic-field relaxometry, etc.
[0105] In some embodiments, MR scanner 110 includes non-inductive
sensing technologies in addition to or instead of RF coils 230,
such as a magnetometer, a superconducting quantum interference
device (SQUID), opto-electronics, etc. Note that non-inductive
sensors may enable sweeping of the magnetic field generated by
magnet 210 without requiring that RF coils 230 be tuned to
different frequencies corresponding to the magnetic-field
strengths.
[0106] The RF signals received by RF coils 230 may be amplified by
RF receive amplifier 232 and detected using RF detector 234. In
particular, RF detector 234 may capture or demodulate the RF
signals to baseband. For example, RF detector 234 may measure MR
signals in their simplest form, such as the free-induction decay of
excited spin states, though it is possible to receive many more
complicated pulse sequences. Computer system 114 may control RF
detector 234 via interface circuit 116 (FIG. 1), network 130 (FIG.
1) and interface circuit 244. For example, computer system 114 may
specify which MR (or RF) signals to capture.
[0107] Note that RF detector 234 may be a linear analog detector, a
quadrature analog detector or a heterodyne receiver. Linear analog
detectors may capture MR signals along one vector in the coordinate
space (e.g., the magnetization along the x or y axis), and a
quadrature analog detector may simultaneously capture MR signals
along two vectors in the coordinate space (e.g., the magnetization
along the x and they axis. In some embodiments, a linear analog
detector includes a doubly balanced mixer, and a quadrature analog
detector includes a pair of double balanced mixers, a pair of
filters, a pair of amplifiers and a 90.degree. phase shifter.
[0108] Furthermore, digitizer 236 may digitize the MR signals
received by the RF detector 234. For example, digitizer 236 may use
a 1 MHz sampling frequency. While this may oversample the MR
signal, digital filtering (such as filtering using by multiplying
by a bandpass filter in the frequency domain or convolving using a
sinc function in the time domain) may be used to capture the
desired frequencies and to remove higher frequency signals. In the
process, the amount of data to be processed and stored by computer
system 114 may be reduced to a more manageable level. However, in
general, a variety of sampling frequencies greater than twice the
Nyquist frequency may be used. For example, there may be up to 1000
samples per MR signal so that a frequency resolution of at least
500 Hz can be achieved. Computer system 114 may control digitizer
236 via interface circuit 116 (FIG. 1), network 130 (FIG. 1) and
interface circuit 244. In particular, computer system 114 may
specify the sampling rate and/or filter settings used by digitizer
236.
[0109] After digitizing, computer system 114 (FIG. 1) may perform a
variety of digital signal processing (such as filtering, image
processing, etc.), noise cancellation and transformation techniques
(such as a discrete Fourier transform, a Z transform, a discrete
cosine transform, data compression, etc.). In general, the MR
signal may specified in the time domain and/or the frequency
domain. Thus, in some embodiments, the MR signal is represented in
k space.
[0110] In one embodiment, the readings from RF coils 230 are
digitized within or just outside of the coil assembly and
transmitted wirelessly to computer system 114 to avoid messy cable
tangling, and without creating significant RF noise in the
frequencies of interest. For example, the data may be transmitted
to computer system 114 at lower or higher frequencies than the
Larmor frequencies of targeted nuclei in individual 112, which may
allow the data to be filtered to exclude noise artifacts.
Furthermore, in some embodiments RF coils 230 are tuned to receive
one or more frequencies. For example, depending on the spectra
desired, a wide-band receiver coil can be used or a software or
hardware-based tuner can be used to automatically tune at least one
of RF detector 234 to receive one or more frequencies from a
desired nuclei or molecule. (However, as noted previously, in other
embodiments an un-tuned receiver, such as a magnetometer, is used.)
Additionally, in embodiments where parallel imaging techniques are
used, different parts of surface coils on individual 112 operate in
parallel to concurrently or simultaneously capture different
spectra.
[0111] Note that biological lifeform holder 214 may support
individual 112 while individual 112 is moved through the magnetic
fields and measured by MR scanner 110. Moreover, as noted
previously, biological lifeform holder articulator 216 may
articulate or move biological lifeform holder 214 as needed to
position individual 112 in relation to the magnetic fields
generated by magnet 210 and magnetic-gradient coils 222. In
particular, biological lifeform holder articulator 216 may rotate
individual 112 in 2D or 3D while individual 112 is being measured
by MR scanner 110 based on instructions received from computer
system 114 via interface circuit 116 (FIG. 1), network 130 (FIG. 1)
and interface circuit 244. Furthermore, as noted previously,
biological lifeform holder 214 may be enclosed in chamber 240 or
may be an enclosed chamber, including a sealed chamber that can be
pumped down to reduced pressure using a vacuum pump or flooded with
an inert gas. In some embodiments, because environmental conditions
can have an effect on individual 112, biological lifeform holder
214 includes sensors that measure temperature, humidity, pressure,
another environmental condition, etc. inside the room, inside
chamber 240 that contains biological lifeform holder 214, or inside
biological lifeform holder 214.
[0112] In some embodiments, biological lifeform holder 214 includes
a tube (or a vessel) and biological lifeform holder articulator 216
includes one or more air jets. These air jet(s) can be used to
manipulate the position of individual 112. For example, the tube
can be made of glass (such as optically clear or transparent
glass), Teflon (which may be transparent at other frequencies of
electromagnetic radiation), or another suitable material. Moreover,
the tube may include features on its outer surface (such as a
texture, fins or other features) that enable individual 112 to be
articulated or manipulated into different positions using a
gripping or interlocking interface to a motor or robotic arm,
thereby allowing system 100 (FIG. 1) to re-orient individual 112
during the indexing or sample-measurement process.
[0113] Moreover, the tube may be inserted into a multi-axis magnet,
such as a multi-axis magnet provided by Cryomagnetics, Inc. of Oak
Ridge, Tenn. Then, system 100 (FIG. 1) can probe or measure
individual 112 from multiple directions, angles, perspectives and
alignments without requiring multiple sensors around bore 236. For
example, individual 112 may be rotated, and a single camera, CCD or
CMOS sensor can capture multiple photographs of individual 112 so
that images of some or all of individual 112 may be captured,
thereby reducing the cost and complexity of system 100, and
improving the reliability. Furthermore, the tube may provide the
chamber that is under vacuum or that is filled with an inert
pre-polarized gas to increase the resolution. In some embodiments,
a low-cost and portable chip-scale device (such as a microfluidic
chip) is used to produce the polarized or magnetized gas, so that
faint MR signals can be detected. For example, as noted previously,
polarized xenon can be used as a contrast agent to enhance images
in MRI of, e.g., human lungs. The polarized xenon atoms may be
produced in the chip by collisions with rubidium atoms that are
illuminated with circularly polarized light. Then, the polarized
xenon may flow out of the chip and may be directed into the tube or
chamber 240.
[0114] While not shown in FIG. 2, in some embodiments MR scanner
110 includes a watchdog or another automatic failsafe safeguard
that monitors MR scanner 110. For example, the watchdog or
automatic failsafe safeguard may monitor the specific absorption
rate of individual 112 using thermal imaging. If a high or
dangerous level of specific absorption is detected (such as one
that may be perceived or that may cause pain or injury), computer
system 114 (FIG. 1), via interface circuit 116 (FIG. 1), network
130 (FIG. 1) and interface circuit 244, may control pulse sequences
to slow down or interrupt a current MR scan.
[0115] Referring back to FIG. 1, computer system 114 may instruct
one or more optional measurement devices 124 to perform other
measurements on individual 112 to obtain physical property
information that specifies a measured physical property of
individual 112, which may be used to determine a diagnostic
classification of individual 112 and/or which may be included in
metadata associated with individual 112. For example, the one or
more optional measurement devices 124 may include: a medical grade
scale that determines a weight of individual 112; a measurement
device that measures one or more dimensions of individual 112 (such
as: a laser imaging system, an optical imaging system, an infrared
imaging system, and/or a spectroscopy system); a light source that
can selectively illuminate individual 112 and a camera-enabled
microscope that acquires or measures one or more optical images of
individual 112 at one or more perspectives, orientations or
lighting conditions; and/or a bioelectric impedance analyzer that
performs a multi-lead measurement of an impedance of individual 112
at DC or an AC frequency (and which may correspond to hydration of
individual 112, and thus may be used to determine or compute the
hydration of individual 112). Alternatively, the hydration or
hydration level, which can affect individual 112, and thus the
invariant MR signature (and the MR signals), may be measured
directly. In some embodiments, the other measurements on individual
112 include: cell cytology, genetic sequencing (such as sequencing
some or all of the DNA in the genome, RNA sequencing or
transcriptomics, gene expression, etc.), transcriptomics, protein
analysis or proteomics (e.g., using mass spectrometry,
metabolomics, liquid chromatography and/or NMR), epigenetic
sequencing, lipidomics, microbiomics, radiomics, cytomics, toxomics
(i.e., measurement of non-biological compounds in individual 112),
an electrical measurement (such as an electrocardiogram, an
electromyogram, an electroencephalogram, etc.), motion detection
(such as body movement), acceleration, one or more vital signs,
computed tomography, electron-spin resonance (which may be used to
measure free radicals), x-ray imaging, ultrasonic imaging (e.g.,
ultrasound), photo-acoustic imaging, infrared imaging or infrared
spectroscopy, other non-destructive measurements (such as radar or
millimeter-wave scanning), activity or behavior data for an
individual (such as data capture using a wearable electronic
device), measurements performed by nano particles in individual
112, chemical composition of fluids (such as blood) measured at
arbitrary locations in individual 112 non-destructively or by
drawing a blood sample (e.g., using microfluidics), another
quantitative or qualitative characteristic or property of
individual 112, etc. Alternatively, computer system 114 may access
data for some or all of these other measurements that are stored in
a remote data structure (such as the biovault) based on the unique
identifier for individual 112.
[0116] Note that the weight and the dimensions of individual 112
may be used to calculate their density. Moreover, the one or more
optional measurement devices 124 may acquire images of individual
cells for inspection and pathology identification. Furthermore, the
medical grade scale may provide information about the chemical
composition and hydration levels of individual 112 if individual
112 is weighed. The weight may be measured before and/or after the
MR scanning (or other imaging operations). In some embodiments,
measuring individual 112 in different portions of the
electromagnetic spectrum may allow a correction for susceptibility
artifacts that may not show in in optical or infrared scans, but
that can occur in certain radio scans.
[0117] In some embodiments, system 100 includes an optional wave
generator 126 that is controlled by computer system 114 via
interface circuit 116. This optional wave generator may generate
ultrasonic waves (and, more generally, mechanical waves) that are
applied to individual 112 during MRE to measure a stiffness of
individual 112. For example, optional generator 126 may generate
waves at one or both ends of bore 236 (FIG. 2) of MR scanner 110 or
may direct waves at one of both ends of bore 236 (FIG. 2) of MR
scanner 110 using a waveguide, such that individual 112 receives
the ultrasonic waves. In some embodiments, the ultrasonic waves
include sheer waves. MR scanner 110 may acquire quantitative MR
fingerprints or images of the propagation of the shear waves
through individual 112, and may process the images of the shear
waves to produce a quantitative mapping of the tissue
stiffness.
[0118] (If, instead of an individual, a tissue sample that is
embedded in formalin fixed-paraffin, then after the invariant MR
signature is determined computer system 114 may transform the
determined invariant MR signature so that it approximates an
in-vivo tissue (i.e., without the formalin or the paraffin. For
example, on a voxel-by-voxel basis, computer system 114 may
subtract a predefined or predetermined invariant MR signature of
the formalin or the paraffin from the determined invariant MR
signature to generate an estimated invariant MR signature.
Alternatively, computer system 114 may correct the parameters in
the MR model on a voxel-by-voxel basis for the formalin or the
paraffin to generate an estimated invariant MR signature. In some
embodiments, a partial volume technique is used to subtract out the
contribution or the effect of the paraffin or wax at borders of the
tissue sample. In particular, computer system 114 may determine
what percentage of a given voxel is paraffin and may remove or
subtract out that weighted portion of the invariant MR signature or
the MR signals that are used to computer the invariant MR
signature.)
[0119] Furthermore, computer system 114 may store the raw data
(such as MR signals from a biological sample or lifeform, the
applied non-ideal pulse sequences, and measured noise), the
invariant MR signature(s) and/or other measurements in the
biovault, such as in memory 120 (which may be locally and/or
remotely located, such as in a cloud-based archive device). In
general, the measured information stored in the biovault may be
sufficiently encompassing to allow the MR model to be trained based
on the scanning instructions (e.g., using training engine 128) and,
thus, the invariant MR signature(s) to be determined. Thus, the
stored information may include different output signals at
different points in the measurement pipeline (e.g., before an
amplifier, after the amplifier, etc.), environmental conditions,
geographic location, etc. The stored information may facilitate
accurate simulations of an MR scan and individual 112, e.g., by
training an MR model.
[0120] The stored information may include or may be associated with
the unique identifier or a new unique identifier generated by
computer system 114 that facilitates subsequent identification, as
well as searching or querying of the biovault. Thus, if individual
112 is subsequently re-measured at a later time, computer system
114 may store the results or differential results (such as any
changes in the invariant MR signatures) so that changes since the
last measurements can also be used for searching. Moreover, the
stored information may include information about the time, location
and/or system parameters (such as information that specifies or
identifies MR scanner 110) when individual 112 was measured. Note
that the stored information may be encrypted. For example,
symmetric or asymmetric encryption based on an encryption key
associated with the unique identifier may be used.
[0121] In some embodiments, computer system 114 optionally compares
the invariant MR signature of individual 112 to one or more other
invariant MR signatures, which may have been previously determined
for individual 112 or another individual. (Alternatively, computer
system 114 may optionally compare a measured MR fingerprint or one
calculated from or based on the determined invariant MR signature
with one or more predetermined MR fingerprints. More generally,
computer system 114 may optionally compare measured MR signals or
those calculated from or based on the determined invariant MR
signature with one or more predetermined MR signals.) Based on this
comparison, computer system 114 may optionally determine a
classification of individual 112 (such as a diagnosis), which may
be stored in the biovault along with or associated with the unique
identifier. Note that the determined or selected classification may
be the one that has the lowest chance of being a classification
error or the lowest matching error. Furthermore, if there are
multiple potential or candidate classifications that have similar
estimated classification errors (e.g., based on a predetermined
supervised-learning model), then the classification of a given
voxel may be determined based on a priori information, e.g., the
classifications of nearby voxels or combinations (such as linear
combinations) of these neighboring classifications, which may help
reduce the classification error of the given voxel.
[0122] The ability to track labels or classifications and outcomes
over time may allow the system to take an invariant MR signature
and look up information that is known about it, such as: how
frequently it is found, in which organs, has it been labeled bad or
good, in which circumstances was it labeled bad or good, etc. In
this way, the metadata about the MR signatures may get richer over
time. For example, an individual (or tissue samples from the
individual) may be indexed every six months. If cancer occurs
during one of these indexing operations, this MR signature may be
labeled `bad.` But what about the classifications of historical MR
signatures in that same region of individual 112? Does the cancer
diagnosis potentially make them pre-cancerous? The system may find
enough evidence, based on multiple MR scans, that the earlier MR
signatures are early indictors of cancer and that there is a path
through the MR-signature space is characteristic of this pathology
evolving over time. Consequently, the biovault may allow such
longitudinal and cross-individual analysis to identify such paths,
which can be use in subsequent classifications and diagnoses, e.g.,
to detect one or more potential anomalies (such as a tumor).
[0123] Moreover, by comparing longitudinally for a particular
individual and/or across individuals within the biovault, the
system may be able to solve problems and assist in identifying
pathologies without requiring the use of a deterministic
machine-learning or supervised-learning model. For example, the
system may be able to differentially identify the presence of a
foreign object (such as screws, pins, joint replacements, etc.)
embedded in individual 112 even if the biovault does not include or
does not have previous knowledge about the foreign object. In
particular, a ferromagnetic material may be detected based on the
resulting magnetic-field distortion, and the invariant MR signature
may include a correction for this magnetic-field distortion.
[0124] In some embodiments, the biovault provides the ability to
aggregate invariant MR signatures on related individuals in other
biovaults without these biovaults sharing other information about
the individuals. This may allow global analytics to be performed on
the individuals in siloed or isolated biovaults.
[0125] (If, instead of individual 112, a tissue sample is measured,
system 100 may use an optional vacuum sealer to enclose and seal
the tissue sample in vacuum in preparation for archival storage.
Moreover, in some embodiments, the tissue sample is formalin
fixed-paraffin embedded after the measurements. Furthermore, a
physical or an electronic label may be attached to or associated
with the tissue sample by an optional labeler to facilitate
subsequent identification. The information in the physical or
electronic label may include the information input and/or extracted
at the start of the measurement technique. In some embodiments, the
tissue sample is destroyed after measurements are made.)
[0126] While the preceding discussion illustrated the use of system
100 to scan or index individual 112, in other embodiments system
100 may be used to scan or index an individual or an animal
multiple times, or multiple MR scans of different persons or
animals. These scans may partially or fully overlap in time (i.e.,
may, at least in part, occur concurrently or simultaneously) to
increase throughput.
[0127] Moreover, while the preceding discussion illustrated the
technician or the MR operator using system 100, in other
embodiments system 100 is highly automated, so that individual 112
may be loaded into MR scanner 110, MR measurements and/or the other
measurements may be performed, one or more potential anomalies may
be detected, an invariant MR signature can be determined,
information may be stored in the biovault, individual 112 may be
removed, and these operations can be repeated for one or more
additional MR scans with minimal or no human action.
[0128] We now further describe determination of an invariant MR
signature. FIG. 3 presents a drawing illustrating an example of
determination of an MR model. The MR model may be a 3D model of
voxels in a portion of an individual (and, more generally, a
biological lifeform), and may include parameters in the Bloch
equations for each of the voxels. In particular, with a
quasi-static magnetic field B.sub.0 along the z axis, the Bloch
equations are
M x ( t ) t = .gamma. ( M -> ( t ) B -> ( t ) ) x - M x ( t )
T 2 , M y ( t ) t = .gamma. ( M -> ( t ) B -> ( t ) ) y - M y
( t ) T 2 , and ##EQU00001## M z ( t ) t = .gamma. ( M -> ( t )
B -> ( t ) ) z - M z ( t ) - M 0 T 1 , ##EQU00001.2##
[0129] where .gamma. is the gyromagnetic ratio, {circle around
(.times.)} denotes a vector cross product and {right arrow over
(B)}(t)=(B.sub.x(t), B.sub.y(t), B.sub.0+.DELTA.B.sub.z(t)) is the
magnetic field experienced by a type of nuclei in the individual.
The parameters in the Bloch equations may include T.sub.1, T.sub.2,
a density of a type of nuclei, diffusion, velocity/flow,
temperature, and magnetic susceptibility. Note that there may be
different parameters for different types of nuclei for each of the
voxels. Moreover, note that the Bloch equations are a
semi-classical, macroscopic approximation to the dynamic response
of the magnetic moments of the type of nuclei in the individual to
a time-varying magnetic field. For example, there may be 67 M cells
in a 1 mm.sup.3 voxel.
[0130] In principle, the solution space for the parameters in the
Bloch equations for the individual may be underdetermined, i.e.,
there may be significantly more parameters to be determined than
there are observations with which to specify or constrain the
parameters. Therefore, the measurement technique may leverage
additional information to constrain or reduce the dimensionality of
the problem. For example, an aspect of the anatomy of the
individual may be determined using other imaging techniques, such
as computed tomography, x-ray, ultrasound, etc. Moreover, tissue
that does not look like (i.e., that has very different MR signals)
than a targeted type of tissue (such as heart tissue) may be
excluded from the MR model. Alternatively or additionally, tissue
that deviates significantly from the expected MR signals based on
previous MR scans (e.g., anomalies or changes) may become the focus
of the MR model, such as by using a contour map (e.g., a cubic
spline) to bound the regions (or specify a boundary of the regions)
where there are significant differences. Alternatively or
additionally, the error between measured MR signals and simulated
MR signals may be represented using one or more level-set
functions, and the boundaries of regions with errors exceeding a
threshold value may be determined based on the intersection of a
plane corresponding to the threshold value and the one or more
level-set functions. In addition, by performing scans at different
magnetic-field strengths B.sub.0 (which may provide similar
information to pseudorandom pulse sequences) using different pulse
sequences and/or different MR techniques, the ratio of parameters
to observations may be reduced, thereby simplifying the
determination of the MR model.
[0131] For example, if a portion of the individual included one
voxel, there may be 4-10 MR model parameters (which specify an
invariant MR signature) that need to be determined for a particular
type of tissue. If the voxel includes M types of tissue, there may
be 4M-10M MR model parameters (which specify M invariant MR
signatures) that need to be determined for the particular type of
tissue. As the number of voxels increases, this can appear to be a
daunting problem.
[0132] However, because different types of nuclei have different
Larmor frequencies, the spatial distribution of the types of nuclei
and their local concentrations may be determined from the measured
MR signals. Then, a predefined anatomical template for the human
body (or a portion of the human body), with associated initial
parameters for an MR model, may be scaled to match the spatial
distribution of the types of nuclei and their local
concentrations.
[0133] Next, for a type of tissue (such as a particular organ), the
MR model parameters may be iteratively refined as the size of the
voxels is progressively decreased (and, thus, the number of voxels
is increased). This analysis may be driven by the error between the
measured MR signals and simulated MR signals using the MR model.
Over time, the focus during the training will be on the residual
regions with errors that are larger than a convergence criterion.
For example, the parameters in the MR model may be trained based on
measured MR signals at one magnetic-field strength and then the
error may be determined based on the predictions of the MR model at
another magnetic-field strength. Furthermore, note that initially
the MR model may assume that there is no contribution or
interaction between different voxels. However, as the error and the
voxel size is reduced, subsequently such contributions and/or
interactions may be included when training the MR model.
[0134] In order to facilitate this fitting or computational
approach, the measurement technique may determine `surface
signatures,` as opposed to 1D signatures. For example, using
measurements at multiple magnetic-field strengths or in the
presence of known magnetic-field disturbances (such as rotation), a
set of MR trajectories may be determined as `fingerprints` that can
be used to determine the invariant MR signature(s). Note that each
MR trajectory may be defined by a magnetic-field function rather
than a fixed magnetic-field strength.
[0135] In an exemplary embodiment, a simulation that is used to
determine the MR model may be vertex/voxel centric. Using a
physical model (such as a Bloch-equation-based model) running at
each vertex, the system may `apply` pulse sequences or disturbance
to the physical model of the individual being scanned. For example,
a message may be broadcast to the vertices that describe the
disturbance in terms of physical laws. Each of the vertices may
compute its predicted change in state and the resulting forces and
energies, which are then relayed as messages to adjacent vertices
about the forces and energies exported from that vertex. When all
the vertices have generated a message, the message has been
forwarded to the adjacent vertices and the state of the system has
been updated, a time interval in the calculation may be complete.
This approach can be generalized so that the message is forwarded
to non-cyclical paths of length N (where N is an integer) radiating
out from the vertex to improve the accuracy of the simulation.
[0136] Once the state has been updated, a computational technique
can be run over the new computed state and then compared to the
measured state. The error may be the difference between the
predicted state and the measured state. As the computational
technique is applied, the system may determine how to optimally
assign the current state to each vertex in a way that reduces or
minimizes the global error. Next, the system may choose a new set
of perturbations for the system and may broadcast these as a new
message to the vertices, as well as executing this disturbance
physically on the individual being scanned. In this way, the system
may provide real-time or near-real-time analysis and feedback
during the measurement technique.
[0137] Thus, the inverse problem of determining the MR model
parameters based on measured MR signals may be `solved` by
minimizing the error or difference between the measured MR signals
and simulated MR signals that are generated based on the MR model,
characteristics of the MR scanner (such as magnetic-field
inhomogeneity) and the scanning instructions used to acquire the
measured MR signals. In some embodiments, the inverse problem is
solved using one or more computational techniques, including: a
least-squares technique, a convex quadratic minimization technique,
a steepest descents technique, a quasi-Newton technique, a simplex
technique, a Levenberg-Marquardt technique, simulated annealing, a
genetic technique, a graph-based technique, another optimization
technique and/or Kalman filtering (or linear quadratic
estimation).
[0138] Note that the inverse problem may be solved using dynamic
programming. In particular, the problem may be divided up and
performed by multiple computers in parallel, e.g., in a cloud-based
computing system. For example, a particular thread may attempt to
solve the inverse problem for particular scanning instructions.
Multiple potential parameter solutions generated by the computers
(or processors) may be combined (e.g., using linear superposition)
to determine an error metric that is minimized using the one or
more computational techniques.
[0139] Moreover, as described previously, the inverse problem may
be solved iteratively by first attempting to find suitable
parameters (e.g., parameters that minimize the error between the MR
signals and simulated MR signals) for the MR model using a coarse
voxel size and then progressively finding suitable parameters with
smaller voxel sizes. Note that the final voxel size used in this
iterative procedure may be determined based on the gyromagnetic
ratio of a type of nuclei being scanned. The voxel size can also be
determined based on the kind of `query` that is made to the
biovault or that forms the based on the MR scan plan, the current
hardware configuration and/or hardware limitations. Furthermore,
the voxel size or locations may also be chosen so that a voxel is
evenly portioned into a set of subvoxels, or so that there is
certain amount of overlap with preview voxel sizes to effectively
oversample; the overlapping region and potentially further localize
where an MR signal originates. As described further below, this
last technique may be akin to shifting the entire gradient system
in one or more dimensions by a distance dx that is less than a
characteristic length of the voxels (such as a length, a width or a
height of the voxels). In some embodiments, the voxel size in the
MR model is smaller than that used in the MR scans (i.e., the MR
model may use a super-resolution technique).
[0140] Additionally, the MR model may include simulations of
dynamics, such as motion associated with: respiration, a heartbeat,
blood flow, mechanical motion, etc. (Thus, there may be additional
terms in the Bloch equations for diffusion, thermomemtry,
spectroscopy, elastography, etc. Consequently, the MR model may be
based on the Bloch-Torrey equations, etc.) For example, when a
voxel contains a space that has a fluid flowing through it (such as
in a vein), the flow of the liquid may be simulated by building a
map of the flow directions and velocity magnitudes in the
individual being scanned to be accounted for it the computation of
the invariant MR signature. Furthermore, when scanning a human or
an animal, the MR model may include the resting motion (such as
that associated with respiration, a heartbeat, etc.). As noted
previously, in order to facilitate calculation of the MR model,
measured MR signals and/or other temporal measurements may be
synchronized with or relative to a reference clock or a biological
time period.
[0141] The MR model may be used to predict how the individual's
body will respond to particular scanning instructions In
particular, the MR model may be used to simulate or estimate the MR
signals for a particular MR scanner having particular
characteristics, for particular scanning instructions and/or for a
particular individual (who has a medical history, previous MR scan
results, etc.). Stated different, an invariant MR signature (which
is based on the MR model) may be used to determine representations
or projections (i.e., the MR signals) in particular contexts, such
as based on the particular characteristics of the MR scanner, the
particular scanning instructions and/or the particular
individual.
[0142] Thus, the MR model may allow system 100 (FIG. 1) to perform
active learning. In particular, the MR model may be iteratively fit
or determined based on `queries` generated by a learning system or
a learning engine (which may be implemented in computer system 114
in FIG. 1). In particular, the queries generated by the learning
engine may include different magnetic-field strengths B.sub.0,
different electromagnetic pulse sequences and/or different
ultrasonic pulse sequences that are based on confidence intervals
for parameters in the MR model. Consequently, the learning engine
may use the measured MR signals in response to these queries to
determine unknown parameters in the MR model and/or parameters
having a poor accuracy (such as a confidence interval greater than
0.1 1, 5 or 10%). More generally, the adaptive learning performed
by system 100 (FIG. 1) may be based on a wide variety of
measurements, such as optical/infrared spectroscopy, x-ray,
computed tomography, proton beam, photoacoustic, ultrasound,
etc.
[0143] While the preceding discussion used the Bloch equations as
an illustrative example, in other embodiments full Liouvillian
computations (such as a Liouville supermatrix of interactions
between two or more elements) or another simulation technique are
used. Note that the MR signals computed or predicted using the MR
model may be sampled at a rate equal to or higher than twice the
Nyquist frequency of MR signals acquired during an MR scan.
[0144] In an exemplary embodiment, computer system 114 (FIG. 1)
first approximates the parameters in the MR model and computes the
error (or difference vector) between the measured MR signals and
simulated MR signals based on this initial MR model. Note that when
there are multiple candidate parameter solutions (having similar
errors) to the inverse problem for a thread corresponding to
particular scanning instructions, computer system 114 (FIG. 1) may
keep the candidates (i.e., a unique parameter solution may not be
identified at this point in the calculation). Alternatively, if
there is no unique parameter solution within a desired error range
(such as less than 50, 25, 10, 5 or 1%), the best (least-error)
parameter solution may be kept. In addition, when there is no
parameter solution within the desired error range, computer system
114 (FIG. 1) may modify the scanning instructions.
[0145] Moreover, computer system 114 (FIG. 1) may compute first and
second derivatives along a surface(s) of parameter solutions in the
individual. (In order to facilitate calculation of a derivative,
note that the parameters may be represented using one or more
level-set functions.) A set of voxels along the line where the
first derivative is zero may be identified. This set of voxels may
be fit using a cubic spline with a minimum error between the voxel
positions and the cubic spline. This fitting operation may be
repeated at all the boundaries in the parameter-solution space.
Moreover, the largest continuous surface within the boundary
defined by the cubic splines may be determined and the
parameter-solution calculation may be repeated to determine a new
continuous surface that is within the previous continuous surface.
This generalized framework may minimize the error across
intra-voxel volumes, thereby improving the agreement between the MR
signals and the simulated MR signals based on the MR model.
[0146] We now describe embodiments of how to determine a
distribution of types of tissue. Using MRF as an illustration,
define a dictionary D.sub.mrf of measured time sampled MR
trajectories (or vectors) for different types of tissue dj (for j=1
to n) such that a measured MR signal y.sub.obv for a voxel can be
expressed as
y obv = j = 1 n .alpha. j d j + , ##EQU00002##
where .alpha..sub.j are normalized weights
( i . e . , j = 1 n .alpha. j = 1 ) ##EQU00003##
and .epsilon. is an error (i e .epsilon.=(y.sub.j, .alpha..sub.j),
for j=1 to n. This may define an intra-voxel linear equation
problem. A generalized inter-voxel problem may model a set of
voxels (such as a cube with 27 voxels) as a graph G. As shown in
FIG. 3, every voxel in the set may have 26 edges to eight adjacent
voxels. A parameter solution to the inverse problem may be defined
as one that minimizes the error.
[0147] Consider the case of two adjacent voxels u and v. The
intra-voxel linear equations U.sub.y and V.sub.y need to be solved
at both u and v. There are several possible outcomes. First,
U.sub.y and V.sub.y may have unique parameter solutions (where a
`unique parameter solution` may be a best fit to an existing MR
model, i.e., with an error or difference vector that is less than a
convergence criterion) and the analysis may be finished.
Alternatively, U.sub.y may have a unique parameter solution but not
V.sub.y. It may be possible that the parameter solution for U.sub.y
imposes a constraint on V.sub.y such that V.sub.y has a single
parameter solution, in which case the analysis may be finished.
However, neither U.sub.y and V.sub.y may have unique parameter
solutions, in which case combining the systems of equations (i.e.,
effectively increasing the voxel size) may yield a unique parameter
solution. Moreover, neither U.sub.y and V.sub.y may have any
parameter solutions, in which case the intra-voxel problem cannot
be solved without further constraints.
[0148] In the last case, it may be possible to look at an adjacent
voxel w, i.e., series voxels u, v and w, with the corresponding
intra-voxel linear equations U.sub.y, V.sub.y and W.sub.y need to
be solved at u, v and w. Note that the intra-voxel linear equations
V.sub.y and W.sub.y reduce to the previous case. When the
intra-voxel linear equations do not reduce to the previous case,
this paring operation can be applied recursively until it does and
then the intra-voxel linear equations can be solved as described
previously.
[0149] In general, this computational technique may be isomorphic
to the problem of fitting a 3D surface (or volume) to minimize the
error. One challenge in this regard is that it assumes that all
adjacent volumes have an equal effect on the parameter solution
.alpha..sub.j that minimizes the error.
[0150] The minimization of the error may initially assume that
there is no inter-voxel contribution (i.e., that the voxels are
independent). Subsequently, inter-voxel contributions may be
included. In particular, considering adjacent voxel volumes, there
are two distinct classes. Volumes that share a surface and volumes
that only share a 1D edge. The minimization function can be
improved by weighting the error contribution at voxel u at the
center of the relative co-ordinate system. If the effect on the
error is proportional to r.sup.-2 (where r is the distance between
center points of voxels) and assuming 1 mm isotropic voxels in the
weightings, the minimization or fitting problem with inter-voxel
contributions can be expressed as
min ( error ( y ( 0 , 0 , 0 ) , .alpha. ( 0 , 0 , 0 ) + 1 ( 1 ) 2 k
= 1 m error ( y k , .alpha. k ) + 1 ( 2 ) 2 l = 1 p error ( y l ,
.alpha. l ) , ##EQU00004##
where the summation over k is for adjacent voxels sharing a common
surface (i.e., (-1,0,0), (1,0,0), (0,-1,0), (0,1,0), (0,0,-1) and
(0,0,1)) and the summation over l is for a remainder of adjacent
voxels sharing a common edge. The assumption in the analysis is
that the most difficult place to fit or determine parameter
solutions is at discontinuities or interfaces between different
tissues. Consequently, during the measurement technique, computer
system 114 (FIG. 1) may solve these locations first and then may
solve the remaining locations.
[0151] Alternatively, because the magnetic contribution from
neighboring voxels is proportional to r.sup.2, given a sphere of
radius R from the center of a primary or central voxel in the
minimization problem, surrounding voxels may be weighted based on
the how much the sphere expands into the volume of the adjacent
voxels (and, thus, based on how strong their inter-voxel
contribution is estimated to be). For example, there may be three
different weights that need to be assigned, including: a weight for
voxels that share a 2D surface, a weight for voxels that share a 1D
line, and a weight for voxels that share a 0D point. Because there
may not be a uniform tissue distribution within each voxel, the
weights may be dynamically adjusted to model different kinds of
distributions inside each voxel in order find the distributions
that minimize the error. This may provide the ability to identify
multiple MR signatures within a single voxel for different types of
tissue. Note that, as computational power increases, the accuracy
of the predictive model may increase and the computational
technique used to solve the minimization problem (and, thus, the
inverse problem) may be modified.
[0152] Thus, in embodiments where the invariant MR signature of a
voxel depends on the invariant MR signatures of surrounding or
neighboring voxels, the invariant MR signature of a voxel may be
computed using 2.sup.nd or N.sup.th-order effects. For example, if
there are N 1.sup.st-order invariant MR signatures (where Nis an
integer), there may be as many as N!/(N-27)! 2.sup.nd-order
invariant MR signatures (if all the voxels interact with each
other). In some embodiments, locality is used to simplify the
inverse problem. In this way, an invariant MR signature may be
generated by incorporating how the invariant MR signatures in
adjacent voxels effect the invariant MR signature in a primary
(central) or 1.sup.st-order voxel.
[0153] In some embodiments, a dithering technique is used to
overcome the arbitrary locations of the voxels relative to the
distribution of types of tissue in the body. In particular, there
may be two or more types of tissue in a voxel because of the
arbitrary voxel placement or the current voxel size. This may
significantly change the MR model parameters for this voxel. This
may suggest that there is more than one invariant MR signature
needed for the voxel. As described previously, in order to confirm
this, the voxels may be displaced by a distance dx (which is a
fraction of the voxel length, width or height) and the MR model
parameters may be determined again. In the processes, the tissue
distribution may be determined. Consequently, this approach may
effectively increase the spatial resolution in the analysis without
changing the voxel size.
[0154] FIG. 4 summarizes the preceding discussion of determining
parameters for one or more MR models that accurately predict MR
signals and their use in the biovault. In particular, MR signals or
trajectories acquired at different magnetic-field strengths may be
combined into a set of MR signals that specify the response to a
surface of magnetic-field strengths. This response may be used to
determine one or more invariant MR signatures 400.
[0155] We now further describe the method. FIG. 5 presents a flow
diagram illustrating an example of a method 1000 for performing an
MR scan, which may be performed by a system, such as system 100
(FIG. 1). During operation, the system may provide, to an MR
scanner, first scanning instructions (operation 510) based on an
initial scan plan to capture first MR signals of one or more first
types of nuclei in at least the first portion of a biological
lifeform, where the first MR signals are associated with first
voxels having first sizes at first 3D positions in at least the
first portion of the biological lifeform.
[0156] Then, the system may receive, from the MR scanner, the first
MR signals (operation 512).
[0157] Moreover, the system may analyze the first MR signals
(operation 514) to detect a potential anomaly in the first MR
signals based on: a medical history of the biological lifeform; an
MR-scan history of the biological lifeform that includes prior MR
scans of the biological lifeform; and/or a first template of a
potential anomaly (such as a multi-dimensional pattern or set of
characteristics associated with the potential anomaly). Note that
the first template of the potential anomaly may include simulated
MR signals of the one or more first types of nuclei at the first
voxels in at least the biological lifeform. In some embodiments,
the system generates the simulated MR signals. For example, the
generating of the simulated MR signals may involve: resampling
predetermined MR signals; and/or interpolating the predetermined
simulated MR signals at the first voxels. Alternatively or
additionally, the simulated MR signals may be generated from a
previously determined invariant MR signature, predetermined
characteristics of the MR scanner and the initial scanning
instructions.
[0158] Furthermore, the system may dynamically modify the initial
scan plan (operation 516) based on the detected potential anomaly,
the medical history and/or the MR-scan history, where the modified
scan plan includes one or more second types of nuclei in second
voxels, having associated second sizes, in at least a second
portion of the biological lifeform, and where the second sizes are
different than the first sizes. Note that at least the second
portion of the biological lifeform may correspond to the 3D
positions of the detected potential anomaly, and/or the second
voxels sizes and at least the second portion of the biological
lifeform may be computed from a size of the detected potential
anomaly.
[0159] Additionally, the system may: provide, to the MR scanner,
second scanning instructions (operation 518) based on the modified
scan plan to capture second MR signals of the one or more second
types of nuclei in at least the second portion of the biological
lifeform, where the second MR signals are associated with the
second voxels at second 3D positions in at least the second portion
of the biological lifeform; and receive, from the MR scanner, the
second MR signals (operation 520). Note that the second voxel sizes
and at least the second portion of the biological lifeform may be
based on a location in the biological lifeform of the potential
anomaly.
[0160] In some embodiments, the system optionally performs one or
more additional operations (operation 522). For example, the system
may generate the initial scan plan for at least the first portion
of the biological lifeform based on the medical history and the
MR-scan history, where the initial scan plan may include the one or
more first types of nuclei in the first voxels, having the first
sizes, in at least the first portion of the biological lifeform.
Moreover, the system may determine a recommended time for a
subsequent MR scan of the biological lifeform based on one or more
of: the medical history; the MR-scan history; and the detected
potential anomaly.
[0161] Furthermore, the system may classify each of the voxels
associated with the detected potential anomaly as having: a risk of
misclassification that is less than a threshold value (such as 1, 5
or 10%); the risk misclassification that is greater than the
threshold value; and/or the risk misclassification that is unknown.
The system may: update, based on additional information (such as
additional MR scans on the same or another biological lifeform,
etc.) the classification; and change the recommended time for a
subsequent MR scan based on the updated classification. For
example, the system may use the analysis of a scan on another
individual to modify the scan plan for the individual. In this way,
as additional scans are performed and the learning in the system is
adapted, this additional knowledge may be applied to other
individual(s).
[0162] Additionally, the system may analyze the second MR signals
to refine the detected potential anomaly based on one or more of:
the medical history; the MR-scan history; and/or a second template
of the potential anomaly (which may be the same as or different
from the first template). Note that the second template of the
potential anomaly may include simulated MR signals of the one or
more second types of nuclei at the second voxels in at least the
biological lifeform.
[0163] Note that the first MR signals may include a first MR image
and the second MR signals may include a second MR image. Moreover,
the second scanning instructions may correspond to: MRS, MRT, MRE,
MRF, and diffusion-tensor imaging. Furthermore, the system may
analyze adjacent voxels associated with the detected potential
anomaly to determine a metabolic chemical signature in MRS.
[0164] Additionally, the analysis of the first MR signals
(operation 514) may include instructions for aligning the first MR
signals in the first voxels with anatomical landmarks of the
biological lifeform in a prior MR scan of the biological lifeform
and comparing the aligned first MR signals in the first voxels with
prior first MR signals in the first voxels in the prior MR scan.
For example, the aligning may involve performing point-set
registration.
[0165] Note that the system may iterative perform, as needed,
additional scans. Thus, the system may: provide, to the MR scanner,
third scanning instructions based on the initial scan plan to
capture third MR signals of the one or more first types of nuclei
in a third portion of the biological lifeform, where the third MR
signals are associated with the first voxels at third 3D positions
in at least the third portion of the biological lifeform; and
receive, from the MR scanner, the third MR signals, where the third
MR signals complete the initial scan plan that was interrupted to
capture the second MR signals.
[0166] Embodiments of the measurement technique are further
illustrated in FIG. 6, which presents a drawing illustrating
communication among components in system 100 (FIG. 1). In
particular, processor 118 in computer system 114 may access
information 610 in memory 120. Using this information, processor
118 may determine a scan plan 612 and scanning instructions 614.
Then, processor 118 may provide, via interface circuit 116,
scanning instructions 614 to MR scanner 110.
[0167] After interface circuit 244 receives scanning instructions
614, processor 616 may execute them, so that MR scanner 110
performs an initial MR scan 618. During MR scan 618, MR scanner 110
may acquire or capture MR signals 620, which are provided to
computer system 114.
[0168] Processor 118 may analyze MR signals 620 to detect one or
more potential anomalies 624. This analysis may involve:
registration, alignment, segmentation, simulation of MR signals,
and/or comparison of MR signals 620 with one or more templates.
During the analysis, processor 118 may access additional
information 622 in memory 120.
[0169] Based on the one or more potential anomalies 624, processor
118 may dynamically update scan plan 626. Then, processor 118 may
determine updated scanning instructions 628, which are provided to
MR scanner 110.
[0170] After MR scanner 110 receives scanning instructions 628,
processor 616 may execute them, so that MR scanner 110 performs MR
scan 630. During MR scan 630, MR scanner 110 may acquire or capture
MR signals 632, which are provided to computer system 114.
[0171] Note that processor 118 may repeat one or more of the
aforementioned operations until the MR scan(s) of the individual
are completed and/or a desired accuracy of one or more detected
potential anomalies 624 is achieved. Furthermore, processor 118 may
determine classification(s) 634 of one or more potential anomalies
624 and/or an invariant MR signature 636 based on the measured MR
signals, which is stored in memory 620. Processor 118 may also
store the MR signals, metadata and other related information in
memory 620.
[0172] In addition, computer system 114 may provide information 638
about the MR scan(s) to a third party (such as a radiologist), such
as to a computer 640 associated with the third party. Subsequently,
computer 640 may provide feedback 642 from the third party that is
used to update the current scan plan, a future scan plan, a
recommended future scan time, one or more templates, etc.
[0173] In some embodiments of one or more of the preceding methods,
there may be additional or fewer operations. Furthermore, the order
of the operations may be changed, and/or two or more operations may
be combined into a single operation.
[0174] In an exemplary embodiment, the system determines an initial
scan plan. As described further below, the initial (as well as
subsequent) scan plan may be based on information, such as: family
history, personal medical history, previous scans, previously
detected anomalies, previous medical lab test results (such as
blood tests, biopsies and other tissue samples, urine tests, etc.),
previous medical imaging results (x-rays, CT scans, ultrasound,
etc.), previous scanning instructions (such as a recommended scan
time), doctor's instructions (such as an instruction to scan the
kidney), requests from an individual (such as a report of knee
pain), information that specifies one or more risk factors for
different pathologies, etc.
[0175] Because hydration can affect the quantitative MR scan
results, the system may acquire additional information before a
scan. For example, the system may measure a hydration level, can
use a medical-grade scale and/or impedance measurements to
determine a body-fat percentage.
[0176] The initial scan plan may indicate or specify a whole or
full-body scan (head-to-toe) of individual. Based on the initial
scan plan, the system may determine scanning instructions, such the
3D voxels. These voxels may be isometric and may have a size (such
as 1 mm.sup.3). In addition, the scanning instructions may specify
spectroscopy of each voxel for types of nuclei including, but not
limited to: .sup.1H, .sup.2H, .sup.23Na, .sup.31P, .sup.14N,
.sup.13C, .sup.19F, .sup.39K, and/or .sup.43Ca. However, these
numerical values and types of nuclei are used as illustration, and
other numerical values and/or types of nuclei may be used as
technology improves or based on the abundance and gyromagnetic
ratios of different types of nuclei. In particular, different
voxels sizes may be used depending on the type of nuclei used, such
as based on the region of the body and the pathology. Thus, the
part of the individual's body being scanned can be an important
factor in determining the voxel size(s) and/or the spectra chosen
for imaging.
[0177] For example, some rare nuclei or nuclei that vary widely
between parts of the body (e.g. calcium) can require a larger voxel
size to get a strong enough signal with MRSI. As shown in FIG. 7,
an original voxel 710 may be upsampled using measurements from
offset voxels 712. Note that the front half of original voxel 710
(with respect to the y-z plane in Cartesian coordinates) is shown
in the FIG. 7 (the rear half is not shown). Moreover, the
upsampling may be arranged so as to divide original voxel 710 into
eight regions that each overlap with eight of the offset
voxels.
[0178] In FIG. 7, original voxel 710 is upsampled with 2.times.
oversampling. However, other values of the upsampling rate may be
used, such as 1.25.times., 2.times., 3.times., 4.times., 6.times.,
8.times., etc. Furthermore, the offset voxels may be uniformly
offset by half of the voxel size of original voxel 710 along each
coordinate, or may be offset by variable amounts for each
coordinate. While the voxels shown in FIG. 7 are isometric, in
general the voxels may be non-isometric. For example, the voxels
may have rectangular dimensions to capture patterns in MRS along a
particular dimension, such as the spectra of glucoCEST
molecules.
[0179] One advantage of upsampling is that it enables original
voxel 710 to be compared (via addition, subtraction or other
operations) to offset voxels 712 to create an interpolated map of
the presence of rare nuclei that require larger voxel sizes.
Upsampling can also enable chemical shifts, spin-spin interactions,
and J-coupling to be reduced, filtered out, subtracted out or
canceled out to reduce noise. For example, calcium may be detected
in the heart using larger voxel sizes (because calcium occurs less
frequently in healthy hearts). Then, multiple offset voxels can be
captured for each voxel in the heart with a slight offset relative
to the larger-sized voxels. The spectra in each original voxel and
the relatively offset voxels may be averaged or subtracted from one
another to interpolate a finer resolution picture of calcium in the
heart, which can be indicative of the presence (or absence) of a
calcified valve or another condition.
[0180] Similarly, in some embodiments, oversampling can be
performed by capturing voxels of a particular size, and then
capturing voxels of a smaller size within the same area. This may
allow finer imaging of an area of interest or of a potential
anomaly detected using a larger voxel size.
[0181] Depending on the desired information specified (directly or
indirectly) in the initial scan plan, the system may include
different types of nuclei in the scanning instructions. For
example, the metabolites or properties that can be detected using
.sup.1H nuclei may include: total choline, lactate, lipid,
N-acetyle-aspartate, citrate, extracellular pH (pHe), treatment
efficacy, detection of metastasis, and tissue oxygen level
(pO.sub.2). Moreover, the metabolites or properties that can be
detected using .sup.19F nuclei may include: drug pharmacokinetics,
pHe, pO.sub.2, enzyme activity, and labeled-substrate utilization.
Furthermore, the metabolites or properties that can be detected
using .sup.31P nuclei may include: energy metabolism (such as
nucleoside diphosphates, phosphocreatine, or inorganic phosphate),
intracellular pH (pHi), and phospholipid metabolism. Additionally,
metabolites or properties that can be detected using .sup.13C
nuclei may include labeled substrate, such as drug pharmacokinetics
and metabolic pathways. Note that the detection accuracy of .sup.1H
and .sup.19F in MRS is typically within the millimolar range of the
detected metabolite. In general, higher concentrations are
typically required for less sensitive types of nuclei, such as
.sup.31P and .sup.13C. In some embodiments, another type of nuclei
is used, such as: .sup.7Li, .sup.14N, .sup.15N, .sup.17O,
.sup.27Al, .sup.29Si, .sup.57Fe, .sup.63Cu, .sup.67Zn, and/or
.sup.129Xe.
[0182] Moreover, the data captured for each voxel can include
T.sub.1-weighted images, T.sub.2-weighted images, fat-suppressed
images, diffusion-weighted images (which may measure the Brownian
motion of water molecules in a voxel), and/or chemical-shift images
to detect the chemicals that the nuclei are in (such as fat versus
water).
[0183] Furthermore, contrast agents injected into an individual or
a tissue sample can also be targeted for detection. For example,
after an individual has been injected with a contrast agent (such
as gadodiamide or gadobutrol), during an MR angiography scan (and
using a moving table) a sequence of vessels may be scanned in
order, including: supraaortal vessels, crural vessels, the
thoracic/abdominal aorta, the abdominal aortal/iliac artery, the
femoral/popliteal artery, etc. However, because of the improved
resolution with stronger magnetic-field strengths (such as 3 T, 5
T, 7T, or larger), contrast agents may be less important and
possibly unnecessary. Note that whole-body MR angiography can
provide information about atherosclerosis, arterial stenosis,
occlusion of arteries, and other vascular information.
[0184] Alternatively, more benign substances can be used as a
contrast agent. For example, an individually may orally consume
sugar (glucose) prior to a scan, and the metabolization of the
glucose can be measured across tissues. Voxels of tissue that
contain faster metabolic rates may be indicative of pathologies
such as cancer, enabling the glucose to function as a contrast
agent. When imaged, these metabolic rates can show tumors `lighting
up` or being illuminated and detected as potential anomalies or
areas to monitor. In some embodiments, a non-injected contrast
agent is used in an individual's lungs, nasal cavities or other
air-filled cavities to allow 3D imaging. In particular, the
individual may breathe a mixture of oxygen and helium. The helium
can provide a stronger signal-to-noise ratio and may enable imaging
of the lungs, nasal cavities or other air-filled cavities in the
body. In another example of a contrast agent, nanoparticles of
diamonds (e.g., diamond dust) can be administered to an individual
(either orally or intravenously) to enable hyperpolarized .sup.13C
imaging.
[0185] As described previously, during the MR measurements based on
the scanning instructions, a suit that contains surface coils and
other measurement devices can be controlled by software executing
the scanning instructions in the system, so that certain modalities
can be turned off and on in real-time as needed. This capability
may allow the scan plan to be modified in real-time based on data
from the current scan, so that the system can collect additional
information using the additional sensors, apparatuses and
modalities.
[0186] For example, if a potential anomaly is detected in the
chest, the system may decide to send an ultrasonic wave through the
chest of an individual to take an MRE measurement of the potential
anomaly or the surrounding region. In this example, the surface
coils may include multiple sensors and data collection equipment
that can be used for specialized anomaly detection. Thus, the suit
may include sensors and RF coils that can be optimized for parallel
collection of data in different measurements and MR techniques,
such as: MRF, MRE, MRS, MRT, multi-nuclear imaging of two or more
nuclei (such as .sup.1H, .sup.23Na, .sup.31P, .sup.13C, .sup.19F,
.sup.39K and/or .sup.43Ca), diffusion-tensor imaging, motion
detection (e.g., using a thermal sensor or MRI imaging to capture
motion of a body part, such as the hear, lung, a joint, etc.),
heart-rate capture, electroencephalogram, and/or integrated EKG,
optical and thermal sensors for motion detection, N-channel
scanning, etc.
[0187] After acquisition of the MR signals, the system may perform
signal-processing operations on the data to: reduce noise, improve
the visibility of a particular scan (e.g., suppression processing),
display the scan data on a display for an operator, analyze the
scan data, perform segmentation on the scan data, register the scan
data with historical scan data stored in memory, and/or another
operation (such as determining an invariant MR signature). For
example, the system may perform noise cancellation on received
data. In particular, if an optical detector (such as a camera or an
imaging sensor) captures motion (such as fine movements associated
with breathing and/or heartbeats), the system can use this
information to determine correction factors to the received data to
reduce noise. Thus, when motion associated with a heartbeat is
detected, the system may perform a transformation and may correct
the MR signals (e.g., using a point-set registration between
adjacent volume slices) to compensate for the detected heartbeat
motion, to reduce artifacts and to provide improved image
quality.
[0188] Then, the system may perform anomaly detection. As described
further below, the system may compare MR signals (and/or one or
more invariant MR signatures) from a current partial or complete
scan against measured or simulated MR signals (based on one or more
invariant MR signatures) for a historical scan, and may flag
unexpected changes or changes that match predefined templates as
potential anomalies. This may involve comparing registered and
segmented portions of MR images to detect: changes in the size of
segments or nodules/growths/swelling or other abnormalities,
anomalies in MR spectrograms, anomalies in MR angiograms, anomalies
in metabolic rates between adjacent voxels in a tissue, etc.
[0189] If no potential anomalies are detected in the current MR
scan, the processing may end, and the scan results may be stored,
e.g., in the biovault. Alternatively, if a potential anomaly is
detected, the system may update the scan plan accordingly. In some
embodiments, even if a potential anomaly is not detected, the
system may update the scan plan based on feedback, such as from a
radiologist and/or based on the results of MR scans of one or more
other individuals in the biovault.
[0190] Based on the resulting updated scanning instructions, the
system may perform a smaller, faster, more specialized or more
targeted scan focused on the potential anomaly. The second MR scan
may be a more detailed scan at a second set of voxel sizes (that
are different from those used in the initial scan) to improve the
visibility or detectable detail. Moreover, the second MR scan may
focus on a different type of nuclei (e.g., nuclei having a
different resonant frequency) and/or may use a different type of MR
technique to determine more information about the potential
anomaly.
[0191] For example, if a potential anomaly is detected in breast
tissue based on the first (initial) scan, the potential anomaly
could be a tumor, or it could be a small calcium cyst. The updated
scan plan may seek to answer this question. Consequently, the
second scanning instructions may look for particular metabolites
using MRS to determine if the tissue outside of but proximate to
the potential anomaly has a slower metabolic rate than the tissue
inside the potential anomaly (which could indicate that the
potential anomaly is a tumor). Alternatively or addition, the
second scanning instructions may modify the MR frequency and may
attempt to detect calcium nuclei within the potential anomaly to
determine the likelihood that the potential anomaly is a calcium
cyst.
[0192] As mentioned previously, in some embodiments the measurement
technique uses breadth-first or dynamic indexing as a form of
compressed sensing. Thus, different spatial resolution or voxel
size (or a set of voxel sizes) may be used in different regions or
in an initial or first MR scan versus a subsequent MR scan.
(However, in some embodiments the same set of voxel sizes is used
for the first scan and the second scan.)
[0193] We now provide some additional examples of in-depth scans
that may be performed based on external conditions. In particular,
when a patient reports knee pain, the scan plan may be updated so
that system performs a second MR scan on either or both knees based
on second scanning instructions that include a smaller voxel size
than in a first scan to capture more information and higher-quality
MRI images. Alternatively or additionally, the second MR scan may
detect a different type of nuclei or may perform MRS to monitor the
cartilage present in the knee(s).
[0194] Moreover, if a blood test indicates a malfunction or disease
of the liver, the scan plan may be updated so that the second MR
scan focuses on the liver with a smaller voxel size or performs MRS
monitoring of metabolites in liver tissue. Furthermore, if a lesion
is detected on the lymph node of an individual, the system may
update the scan plan to collect more of the region around and
including the lesion in the phosphorous spectrum to determine if
the lesion is metabolizing faster that the surrounding tissue. The
system may also perform diffusion-weighted imaging of the lesion,
which could help to identify a malignancy. Additionally, if a
lesion is detected in the breast of an individual, the system may
update the scan plan to collect more of the region around and
including the lesion in the calcium spectrum, because calcium
deposits in breast tissue can be a precursor to breast cancer. The
system may also perform imaging in the phosphorous spectrum to help
to determine how the lesion is metabolizing with respect to the
surrounding tissue.
[0195] In some embodiments, if an individual is overweight and a
large amount of visceral fat is detected in the first scan, the
scan plan may updated to perform a detailed scan of the pancreas to
look for signs diabetes. Moreover, if calcification is detected on
the aortic valve in the first scan, the scan plan may be updated to
perform blood-flow analysis looking for a weakened vessel or a
micro aneurism. Furthermore, if an anomalous difference in femur
lengths is found in the first scan, the scan plan may be updated to
perform a detailed scan of the individual's hip and knee cartilage
in the sodium spectrum to look for signs of arthritis.
Alternatively or additionally, if the system detects a rotation of
the individual's pelvis within inflamed musculature in the first
scan, in the second MR scan the system may look in more detail for
structural issues in the individual's hip and/or spine.
[0196] Note that if the system detected white-matter lesions in the
brain that can be an indicator of multiple sclerosis, the system
perform a second MR scan at a different resolution or using spectra
focused on the region containing the white-matter lesions in an
attempt to identify justracoritcal lesions or other indicators of
multiple sclerosis to differentiate against other pathologies that
may be vascular or age related. The likelihood of one or the other
pathology maybe indicated by additional data in the individual's
medical history. For example, if the individual is very young, it
may indicate a stronger need to do more detailed scanning rather
than if the patient is very old and has no other symptoms of
multiple sclerosis.
[0197] In another example, lesion detection in the prostate may
rely heavily on functional imaging of the prostate and lesion
staging may rely on high-spatial-resolution imaging of the prostate
as well as a characterization of the remainder of the pelvis.
Therefore, another region of the body (such as the pelvis) may be
included in the second MR scan to aid in the lesion staging.
[0198] In some embodiments, flow-velocity mapping/modeling is
followed by MRS to determine a kind of infarction. In particular,
analysis of flow parameters in the MR model may allow an
obstruction in a vessel to be identified. The location of an
infarction in a blood vessel (such as an artery or a vein) may be
determined without directly measuring the flow based on changes in
blood flow velocities or parameters in the MR model that indicate
increased blood pressure or turbulence. Moreover, based on
Bernoulli's law, the narrowing of a blood vessel can be inferred
without directly imaging plaque or a thrombosis. Then, the accuracy
of this determination can be increased by performing MRS in the
identified region to see if there has been an increase in the
chemical signature expected from plaque buildup.
[0199] The MR signals acquired in the second MR scan may be
processed using the same or similar signal-processing and analysis
techniques as the MR signals from the first scan. If an additional
potential anomaly is detected, the system may repeat at least some
of the aforementioned operations and may perform a third scan. For
example, if a second, fine-resolution scan of hydrogen nuclei
(after the first scan of hydrogen nuclei) indicates that additional
detail about a potential anomaly is needed, a third scan of sodium
nuclei may be performed. Alternatively or additionally, MRS may be
used to determine if a metabolite is present, or an MR angiogram
may be used to confirm potential anomalies in blood-vessel walls.
Therefore, at least some of the operations in the measurement
technique may be repeated as addition potential anomalies are
detected and/or when addition information related to a potential
anomaly is needed. In some embodiments, a cycle-detection mechanism
or module prevents the system from repeatedly detecting the same
potential anomaly and/or repeatedly updating the MR scan plan,
e.g., preventing an infinitely recursive loop.
[0200] In these ways, the system may iteratively detect and
classify potential anomalies in the individual. Note that the
potential anomaly and/or an additional potential anomaly may be
highlighted for review by a physician, a radiologist and/or other
healthcare provider or specialist.
[0201] In some embodiments, instead of or in addition to updating
the current scan plan, the system updates a future scan plan,
determines a recommended future scan time (or a return date for the
individual) and/or sends out a calendar invite or another
notification to the individual. For example, the objectives of the
future scan plan and/or the recommended scan time may be based on
analysis of an individual's risk factors (such as a determined risk
score) for one or more pathologies and any anomalies that were
detected. In particular, an individual with a detected anomaly may
be instructed to return within a month or six months for their next
scan. Alternatively, the future scan may be scheduled for 30
minutes after the completion of the current scan, and the
individual may be instructed to consume chocolate to prime the
individual's body with glucose before a scan focusing on
metabolites 30 minutes later. The future scan plan may also include
looking for additional anomalies highlighted for review by a
physician, a radiologist or another healthcare profession.
Furthermore, the scheduling of the future scan time (i.e., the
recommended scan time) may be based on the availability of the MR
scanner, the individual's personal schedule/calendar and/or one or
more healthcare professionals' schedule/calendar.
[0202] Moreover, after the MR scan(s) and analysis are completed,
the system may generate a summary report about the individual's
health, including the most recently collected data, as well as some
or all of the historical data. These reports can include suggested
follow-up actions, such as, when the patient should return for a
follow-up visit (such as the recommended scan time) or a
recommendation to see a medical specialist to further review the
data collected about a potential anomaly. For example, if the
system detected a cardiac anomaly, such as calcification of the
aortic valve, the system may recommend seeing a cardiologist. These
recommendations may be mediated by a human operator, a healthcare
professional (such as a physician), a user interface displayed on a
display and/or via a mobile application.
[0203] The summary report may also compare the individual health
and MR scan data to a larger population, such as the relative brain
mass for the individual compared to other individuals of the same
age, gender and body mass. Alternatively or additionally, the
system may report that the amount of fatty tissue in and around the
individual's liver has steadily increased over time and indicate
the associated risks, as well as things the individual can do to
reduce visceral fat in the body. Moreover, the summary report may
indicate increases or decreases in lean muscle mass in certain
muscles, a list of pathologies for which the individual is
statistically at risk and actions that can be taken to reduce these
risks. Thus, in the case of the cardiac anomaly, if calcification
of the aortic valve is detected, the system may recommend a
specific cardiologist or a list of cardiologists (such as
cardiologists in the area, who are closest, who are available, who
have the lowest cost, who are the highest rated, etc.). In some
embodiments, with approval from the individual, the system may
schedule an appointment and/or share relevant data that has been
collected with the cardiologist.
[0204] As noted previously, in some embodiments, the first or
initial MR scan is paused in response to an interrupt from the
system when a potential anomaly is detected. In order to facilitate
subsequent completion of the first MR scan, the position (which is
sometimes referred to as the `position context`) in the first MR
scan may be saved in memory for subsequent use. In addition, the
scanning context of the MR scanner may be saved in memory for
subsequent use. The scanning context of the MR scanner may include:
a table or biological-lifeform holder position, magnetic-gradient
pulse generator settings, RF sources, RF-source frequency settings,
RF pulse-generator settings, and other MR-scanner configuration
information. Note that the MR scanner may have to pause between the
first MR scan and the second MR scan and, optionally, between the
MR scan and a resumed first MR scan to wait for the
magnetic-relaxation times (such as T.sub.1, T.sub.2, and the
adjusted spin-spin relaxation time T.sub.2*) to decay to an
appropriate level to allow spins to re-magnetize to the external
magnetic field.
[0205] In these ways, the system may perform more-detailed scans
(e.g., finer voxels or larger voxels targeting a different type of
nuclei) or additional types of scans (MR angiography, MRS, etc.) in
the middle of a larger scan, such as a general body scan or a
general area scan. For example, if an individual has an involuntary
episode (such as seizure, spasm, etc.) during an MR scan, the MR
scan can capture information from the brain during the seizure.
Information about muscle spasms, blood clots, seizures (epilepsy)
can also be captured by saving the position and/or the scanning
context immediately upon detection of an involuntary episode, and a
second MR scan may be performed to capture information about the
involuntary episode.
[0206] Furthermore, the system may incorporate and/or control
treatment therapies that can be applied to a detected
anomalies.
[0207] We now describe radiologist feedback in more detail. After
an MR scan is completed, while an MR scan is being performed,
and/or when a potential anomaly is detected, the system may provide
information about the potential anomaly, associated metadata and/or
related medical information to one or more radiologists (or other
healthcare professionals) for evaluation, so that the one or more
radiologists (or the other healthcare professionals) can confirm or
correct the identification and the classification of the potential
anomaly (and, more generally, can provide feedback, which is
sometimes referred to as `radiologist feedback`), and can provide
instructions (if any) for a future scan plan or a future scan. For
example, the information about the potential anomaly, the
associated metadata and/or the related medical information can be
provided and the feedback can be received using a distributed
consulting software application or service. Note that a potential
anomaly may be converted or re-labeled as an anomaly after it is
reviewed by a radiologist. However, in some embodiments, the system
may automatically determine if a potential anomaly is, in fact, an
anomaly.
[0208] The radiologist feedback may be used to update the scan plan
and/or when determining the future scan plan. In addition, the
radiologist feedback may be used to update the anomaly detection,
such as the templates or look-up tables used and/or the pathology
information included in the biovault. For example, the changes to
the templates, the look-up tables, and/or the pathology information
may affect analysis of voxels associated with a portion of the
body, a type of tissue, across historical scans for an individual,
a group of similar or related individuals and/or the entire
population of individuals captured by one or more MR scanners.
[0209] In particular, based on the radiologist feedback, the risk
level for look-up table values for voxels characterized as unknown
risk may be changed, the confidence of low-risk and high-risk
values in a look-up table may be verified, reinforced, made more
robust, or otherwise corrected or improved across at least a subset
of the population in the biovault. For example, a radiologist may
rate the stage of cancer in a detected potential anomaly in an
individual's liver, and the look-up table values may be updated or
otherwise verified for the individual, as well as similar
individuals and/or the entire population of individuals scanned by
one or more MR scanners.
[0210] In addition, the look-up table values and/or the pathology
information included in the biovault may be updated based on
information from research publications. This publication
information may be entered manually or automatically by crawling
newly released research papers using a document crawler or using
another learning-software technique. For example, a new research
paper highlighting a detection of a pathology based on metabolic
rates in a type of tissue can be incorporated to update or
reinforce a global anomaly detection technique (such as software, a
program module or an engine). Then, the anomaly detection technique
may be used to generate a look-up table that is used in the
analysis to detect variation in metabolic rates for voxels in a
type of tissue, and the improvement can be applied to some or all
of the individuals that are monitored using the system. In some
embodiments, the updated anomaly detection technique is applied
retroactively to some or all of the existing or historical MR scans
in the biovault. In this way, additional anomalies can be detected
and the future scan plans for individuals with newly detected
anomalies can be updated, which can result in the scheduling of
additional scans, changing the scheduling of existing scans, as
well as other medical responses (such as additional biopsies,
medical lab tests, etc.).
[0211] We now describe the registration and segmentation operations
in more detail. During a scan and/or the subsequent analysis, the
system may perform registration and segmentation of MR signals.
These operations on the acquired or captured MR signals (or the
corresponding invariant MR signature(s)) may be facilitated by
comparisons with historical data in the biovault, which may include
registration and segmentation information from previous
computations. Note that the registration between one or more MR
images (either current and/or historic) can be performed using a
wide variety of registration techniques, such as point-set
registration.
[0212] In some embodiments, in order to use the previous invariant
MR signature to generate the estimated or simulated MR signals, a
registration technique is used to align the individual (or MR
signals acquired for the individual) with reference markers at
known spatial locations or with the voxels in the previous
invariant MR signature. This registration technique may use a
global or a local positioning system to determine changes in the
position of the individual relative to an MR scanner.
[0213] Moreover, the previous invariant MR signature or estimated
MR signals based on the previous invariant MR signature may be used
during virtual registration of the individual. In particular, the
previous invariant MR signature may be used to generate estimated
MR signals for sets of voxels. The estimated MR signals in a given
set of voxels may be averaged, and the resulting average MR signals
in the sets of voxels may be compared to MR signals measured during
a current scan to determine a static (or a dynamic) offset vector.
For example, the positions of the average MR signals in the set of
voxels (such as average MR signals in 3, 6, 12 or 24 regions or
portions of an individual) may be correlated (in 2D or 3D) with the
MR signals in the set of voxels in the current scan. This offset
vector may be used to align the MR signals and the estimated MR
signals during subsequent comparisons or analysis. Alternatively,
the comparisons may be made on a voxel-by-voxel basis without
averaging. Thus, the MR signals for a voxel in the individual may
be compared to corresponding MR signals for the voxel measured on a
prior occasion by performing a look-up in a table. In some
embodiments, the registration or the offset vector of an individual
is computed based on variation in the Larmor frequency and the
predetermined spatial inhomogeneity or variation in the magnetic
field of an MR scanner.
[0214] Furthermore, the registration technique may involve
detecting the edges in node/voxel configurations. Because of the
variability of anatomy across different individuals, transforming
small variations of data into more generalized coordinates may be
used to enable analysis and to generalize the results to a
population. In general, the transforms may be one-to-one and
invertible, and may preserve properties useful for identification
and diagnostics, such as: curves, surfaces, textures and/or other
features. For example, the features may be constrained to
diffeomorphic transformations (such as smooth invertible
transformations having a smooth inverse) or deformation metric
mappings computed via geodesic flows of diffeomorphisms. In some
embodiments, a diffeomorphic transformation between surfaces is
used to compute changes on multi-dimensional structures (e.g., as a
function of time).
[0215] Additionally, linear combinations of diffeomorphic
transformations computed based on sets of matches between MR
signals and simulated MR signals associated with one or more
invariant MR signatures (or linear combinations of invariant MR
signatures) can provide spatial offset corrections based on a piori
estimated information (such as motion, deformation, variations in
anatomy, magnetic field, environmental conditions, etc.). These
spatial offset corrections may be used as a weighted component in a
supervised-learning registration engine. For example, a set of
diffeomorphic velocity fields tracking a set of points across a set
of phases of distortion (caused by movement of the lungs during
regular breathing, the heart during heartbeat motion or a muscle
during contraction or expansion) can be applied to a region of the
body corresponding to the sets of points in the region (e.g., a set
of voxels in or around the heart or lungs).
[0216] Note that registration, segmentation and/or anomaly
detection can be performed sequentially (e.g., in a pipeline)
and/or in parallel.
[0217] We now describe anomaly detection in more detail. The system
may detect discrepancies between the current MR scan and one or
more historical MR scans. For example, the system may compare MR
signals (and/or one or more invariant MR signatures) from a current
partial or complete scan against measured or simulated MR signals
(based on one or more invariant MR signatures) for a historical MR
scan, and may flag unexpected changes or changes that match
predefined templates as potential anomalies. As noted previously,
this may involve comparing registered and segmented portions of MR
images to detect: changes in the size of segments or
nodules/growths/swelling or other abnormalities, anomalies in MR
spectrograms, anomalies in MR angiograms, anomalies in metabolic
rates between adjacent voxels in a tissue, etc.
[0218] For example, the segmented images can include images of the
heart, and if a recent MR image includes a larger heart muscle than
a historical MR image, an enlarged heart may be detected as an
anomaly. A more in-depth scan may be requested in an updated scan
plan at a smaller voxel size, and/or additional MR scans may be
performed using MR angiography, MR colonoscopy, MR venography
and/or MRS to provide additional information for use by a
healthcare professional and/or for use in automated diagnosis by
the system.
[0219] In another example, a first MR scan may include an image of
a colon. If a polyp larger than approximately 8 mm is detected,
which was either new or larger than in a previous MR scans, a finer
resolution scan with a smaller voxel size may be performed to
evaluate a potential colonic carcinoma. Alternatively, if the
system detects a decrease in bone density over time in an
individual complaining of hip pain, the system may image the hip
region in greater detail to look for osteoporosis or fractures.
[0220] More generally, during the analysis the system may use an
anomaly detection technique (such as a supervised-learning
technique, comparisons with a previous MR scan data or information
derived from a previous MR scan data, e.g., comparisons with values
in a look-up table, comparisons with a template, e.g., a target
pattern or set of characteristics that matches a particular
pathology, etc.) to identify potential anomalies and/or
pathologies. For example, the system may detect a potential anomaly
by comparing an output of the anomaly detection technique with a
disease-specific threshold or spatial pattern. Note that the
anomaly detection technique may be trained using information that
specifies risk factors, historical MR scan data, statistics (e.g.,
a mean, a median, a mode, standard-deviation outliers, etc.)
associated with MR signals for voxels in one or more individuals,
pathologies in the biovault and/or radiologist feedback.
[0221] Thus, during the anomaly detection, the system may flag
unexpected changes as potential anomalies, e.g., by comparing
registered and segmented portions of MR images to detect changes in
the size of segments or nodules, growths, swelling or other
abnormalities, detecting anomalies in MR spectrograms, detecting
anomalies in MR angiograms, detecting anomalies in metabolic rates
between adjacent voxels in a tissue, etc.
[0222] In some embodiments, the anomaly detection involves
receiving historical MR scan data, and computing a look-up table
for the voxels in the historical MR scan data. Then, the system may
register 3D image slices of voxels for a current MR scan, and may
compare at least one voxel from the 3D slice of voxels for the
current MR scan with the corresponding entry in the look-up table
for those voxels. Based on the comparison (such as based on a
threshold), the system may classify the voxel as a low-risk voxel,
a high-risk voxel, or as a voxel having an unknown risk.
Alternatively, if the voxel is determined to be cancer, the voxel
may be classified as an early-stage cancer voxel, a later-stage
cancer voxel, or an unknown-stage cancer voxel. Note that an
unknown-risk voxel or an unknown-stage cancer voxel may be flagged
for review by a radiologist or for biopsy. Moreover, low-risk
voxels and high-risk voxels may also be reviewed and verified by a
radiologist or flagged for biopsy, but the classification can help
the radiologist classify and evaluate images faster and more
effectively.
[0223] As noted previously, if a potential anomaly is detected, the
system may update the MR scan plan and the scanning instructions to
include smaller, faster, more specialized or more targeted scans,
scan lines, or partial scans focused on the potential anomaly. The
updated scanning instructions may include scanning remaining voxels
from the first scan at a second set of voxel sizes. Alternatively,
the updated scanning instructions may include rescanning a
previously scanned region at a second set of voxel sizes to improve
the visibility or the detectable detail. Moreover, the updated scan
plan and/or scanning instructions may include one or more different
types of nuclei (e.g., having different Larmor frequencies), a
different type of RF pulse sequence, a different MR technique (such
as MRI, MR angiography or MRS to determine more information about
the potential anomaly.
[0224] We now discuss determination of a scan plan and the scanning
instructions in more detail. As noted previously, the system may
determine a scan plan for the individual based on: age, gender,
family history, a personal medical history, previous MR scans,
previously detected anomalies, previous medical lab test results
(such as blood tests, stool/biome tests, biopsies and other tissue
samples, urine tests, etc.), previous medical imaging results
(x-rays, CT scans, ultrasound, etc.), previous scanning
instructions (such as a recommended scan time), doctor's
instructions (such as an instruction to scan the kidney), requests
from the individual (such as a report of knee pain), information in
the biovault for one or more other individuals (such as individuals
with similar medical contexts, pathologies or risks) and/or, more
generally, information that specifies one or more risk factors for
different pathologies. In order to determine the scan plan, the
system may first determine risk factors or scores based on
information in the biovault.
[0225] In some embodiments, the system may gather information
associated with or specifying the risk factors for the individual.
For example, the individual, a researcher, a medical doctor, a
technician, a nurse, or another healthcare professional may enter
information specifying the risk factors. Alternatively or
additionally, the information associated with or specifying the
risk factors can be accessed from an electronic medical record of
the individual, downloaded from a social-media profile of the
individual and/or may be collected from the individual using a
wearable electronic device (such as a smartwatch, a smartphone, a
personal fitness device, etc.). In particular, the information
collected using the wearable electronic device may include: a vital
sign (such as heartbeat data), pedometer data, sleep data, etc.
[0226] The scan plan may be computed by the system using a
supervised-learning technique that is derived from or trained using
the individual risk factors, historical MR scan data, radiologist
diagnoses/classifications and/or, more generally, information
included in the biovault. The supervised-learning technique may
specify areas of interest within an individual's body and/or values
in a look-up table that are used during analysis of MR signals from
an MR scan. Note that the supervised-learning technique may
include: a support vector machine, classification and regression
trees, logistic regression, linear regression, nonlinear
regression, a neural network, pattern recognition, a Bayesian
technique, etc.
[0227] In some embodiments, when the individual arrives for a
medical appointment or an MR scan appointment, the individual may
access their medical information securely, as well as securely
store the results of their MR scan(s) both before and after the MR
scan(s). For example, an individual may use: a retinal scanner, a
fingerprint scanner, an RFID token, a barcode, a login/passphrase
and/or two-factor authentication scheme, or any other suitable
authentication and authorization token or technique. The
authentication and authorization information may allow the
individual to unlock their medical data and/or to input risk-factor
information to facilitate determination of the scan plan(s). Then,
the system may access the necessary information of the individual,
but may not need to have access to their name, address, phone
number or other explicit personal identifying information. The MR
scan plan can then proceed to scan areas of interest (e.g.,
predicted areas of concern where there may be potential anomalies),
and can store the information securely using an encryption
technique, such as a secure hash, a symmetric or an asymmetric
encryption technique, etc.
[0228] When determining the scan plan, the system may use the
individual's risk profile and MR scan history (if the individual
has been scanned before). For example, if the individual has an MR
scan history, previously detected anomalies or medical problems
encountered since the last MR scan can be used to determine the
scan plan. Based on the scan plan, the scanning instructions may
specify one or more types of nuclei. Moreover, the scanning
instructions may indicate that the MR scanner should perform a scan
of the one or more types of nuclei at a first set of voxel sizes.
The scanning instructions may include or may specify: magnetic
gradients, the MR frequencies of RF pulses associated with a
specific voxel size, a specific MR frequency associated with a
specific type of nuclei, a specific MR frequency associated with a
specific molecule, a specific tissue type, etc.
[0229] For example, the risk factor or score for localized skin
cancer may be increased for a 42-year-old male with no personal
medical history of skin cancer, but a family medical history of
skin cancer, who has two large moles. In addition, an anomaly may
have been detected in their knee in a previous MR scan.
Consequently, the location on their knee may be added to the scan
plan, and the scan plan may specify that the MR scan measure
.sup.19F Fluoride and a smaller voxel size when detecting .sup.1H
nuclei. In this way, the MR scan of the knee can be captured in
greater detail for a radiologist or another healthcare professional
to review.
[0230] The system may compute the voxel size or a set of voxel
sizes in the scanning instructions based on the scan plan.
Moreover, the system may determine the organ(s) or tissue to be
scanned, the location(s) in the body, and the type of nuclei to be
detected. In general, the voxel size may depend on the organ, the
location in the body and the type of nuclei.
[0231] Thus, if the anomaly in the knee was previously detected
using a 1 mm.sup.3 isometric voxel size, a small voxel size may be
selected. For example, the voxel size in the current MR scan may be
a 0.1 mm.sup.3 isometric voxel to capture the anomaly in more
detail and to provide the best possible balance between
identification in the MR signals and the MR signals capture
time.
[0232] As discussed previously, the voxel size may be chosen based
on the type of nuclei that is to be detected. The MR signal that is
measured is primarily limited by the gyromagnetic ratio of the type
of nuclei as well as the concentration of the type of nuclei in the
volume defined by a voxel. Note that it may not always practical to
choose the smallest possible voxel size because increasing the
density of voxels per unit volume can require more encoding
operations, which in turn can result in longer acquisition or scan
times. Therefore, in order to optimize scan times, MR scanner
utilization, and the accuracy of anomaly detection, the system may
pre-define or pre-select a first order set that includes a
`summary` voxel size, MR spectra and pulse sequence(s) that may be
specific to organs or regions in the body. This first order set can
enable an initial or first scan to collect enough information so
that potential anomalies can be at least statistically detected. In
addition, more-detailed information can be collected in real time
(i.e., during an MR scan) when a potential anomaly is detected.
[0233] In general, a number of different factors can be used to
compute the initial summary voxel sizes. This same information can
also be used to determine how to tune the voxel sizes and MR
spectral sensitivities in order to collect more detailed
information when a potential anomaly is detected. We now describe
several of these factors in more detail, including: the
gyromagnetic ratio, T.sub.1 and T.sub.2 relaxation times, estimated
abundance, volumetric size of organs or body structures, medical
risk factors and correlations and/or previous MR scan data.
[0234] The gyromagnetic ratio of a type of nuclei can be used to
estimate the MR signal the system expects to see for a specific
voxel size and MR spectrum within a healthy or a diseased organ, or
another structure in the individual. As described previously, a
variety of metabolites or properties can be detected using
different types of nuclei.
[0235] Moreover, as summarized in Table 1, different tissues can be
characterized by different T.sub.1 and T.sub.2 relaxation
times.
TABLE-US-00001 TABLE 1 Tissue T.sub.1 (s) T.sub.2 (ms)
Cerebrospinal Fluid 0.8-20 110-2000 White Matter 0.76-1.08 61-100
Gray Matter 1.09-2.15 61-109 Meninges 0.5-2.2 50-165 Muscle
0.95-1.82 20-67 Adipose 0.2-0.75 53-94
[0236] Furthermore, different types of nuclei and chemicals are
known to have different nominal concentrations or abundance in
different organs and structures within the body. Certain
pathologies of different organs and structures of the body also
have unique chemical signatures that may contain higher or lower
concentrations with respect to other regions of the body. Knowledge
of this information a priori can be used to aid in determining
optimal voxel sizes and spectral sensitivities for specific organs
and regions of the body based on the purpose of the MR scan, e.g.,
as indicated in the scan plan. As more data is collected, both on
an individual basis and across the general population in the
biovault, the nominal and pathological chemical signatures and
concentrations for different organs can be refined and segmented to
further customize and tune the optimal scan plan for each
individual.
[0237] The volume of structures and organs in an individual can
also be used to computer voxel sizes in order to optimize
acquisition or scan times. For example, it may take longer to scan
the same voxel size in a bigger heart than a smaller heart, so an
adjustment to the voxel size may be proportional to the volumetric
size of organs or body structures. In particular, instead of using
a 1 mm.sup.3 isometric voxel for a median-sized male heart, if the
heart volume for a male is estimated to be 20% larger than the
median heart, then an isometric voxel size of 1.0626 mm on a side
can be used (note that 1.2.sup.1/3 equals 1.0626) to ensure that
the MR scan has a similar scan time.
[0238] Furthermore, medical risk factors and correlations may
include, but are not limited to, age, gender, blood work, urine
samples, stool samples, personal or family medical histories, which
may suggest that certain organs or structures in an individual's
body should be scanned at specific voxel sizes in certain MR
spectra in order to increase the likelihood of detecting anomalies
associated with pathologies for which the individual is potentially
at risk. For example, sodium spectra tend to show up in cartilage,
so when evaluating the knees of an individual that is over age
forty, sodium spectra imaging may be selected to evaluate their
knee cartilage.
[0239] Additionally, data from previous MR scans of an individual
having anomalies or regions of interest that need to be monitored
over time may also aid in determining the optimal voxel size and MR
spectral sensitivities in the scan plan and/or the scanning
instructions for this individual.
[0240] In some embodiments, prior to performing an MR scan, a
series of pre-scan operations may be performed. For example, if the
MR scan is the first MR scan, a more comprehensive questionnaire
and checklist for family history may be included, and permission
may be obtained to retrieve existing medical records and store them
in the biovault with other medical information for the individual.
In addition, a cancer screen may be performed to determine if the
individual has a genetic pre-disposition to specific types of
cancer that should be monitored. Alternatively, if the MR scan is
not the first MR scan, then the previously collected information
and previous MR scans can be used as a guide.
[0241] Moreover, the individual's height, weight, blood pressure,
blood oxygen levels, impedance or hydration level may be measured.
Furthermore, sugar water may be administered to improve contrast in
MM. Then, high-resolution pictures may be captured of the
individual's eyes, nose, throat, ears, and/or skin, as well as a
thermal image of their body. The individual may also provide a
urine sample, a blood sample, a saliva sample and/or a stool
sample, which may be used to assess hydration, and to perform basic
assays, a full blood panel, microbiome analysis, genetic sequencing
or next generation sequencing and/or refractometry (i.e., to
measure the index of refraction). Additionally, the individual may
be asked a series of questions to assess any recent changes in
their health or symptoms they may be experiencing.
[0242] Next, this information, as well as other information in the
biovault, may be used to determine the scan plan and the scanning
instructions. Based on the scanning instructions, the MR scanner
may perform a whole-body MR scan. This MR scan may include:
high-resolution full-body morphology (which may accurately locate
organs and bones in 3D space that can be used as a map in the
remainder of the MR scan or during segmentation of MR images);
whole-body GluCEST with sugar-water contrast; full-body MR
angiography and blood-velocity flow imaging; full-body
diffusion-weighted imaging; full-body susceptibility-weighted
imaging; full-body MRT; MRI of specific body parts, including the
head, chest, heart (with sodium imaging in parallel, which can be
captured using a dual-tuned surface coil), abdomen (including the
liver, kidney, stomach, pancreas, prostate, colon, etc.); and MRE
on the previously disclosed tissues.
[0243] We now describe the biovault and its use in more detail. In
general, tissue samples from most non-diseased individuals in
hospitals are evaluated by a medical specialist (such as a
pathologist) and then destroyed. However, government regulations
and laws often require that certain pathology samples are stored
for a specific amount of time before they can be destroyed.
Currently, there are no large standardized and quantitative
datasets that contain information for symptomatic and asymptomatic
individuals for comparison and improvement of medical diagnoses and
that allow researchers to compare new individuals against an
archive of historical measurements.
[0244] In order to address this need, the indexed MR signals and/or
the associated invariant MR signatures may be characterized and
normalized quantitatively so that their digital representation can
be uploaded to a service where analysis techniques can, in
real-time or near real-time, compare the sample quantitatively to a
vast data structure (such as the biovault, which is sometimes
referred to as a `pathology characteristics knowledge base` or a
`pathology knowledge base`) containing numerous previously measured
and indexed ex vivo samples, in vivo samples and/or information
about individuals (including those for fresh or `wet` tissue
samples, frozen samples, formalin fixed-paraffin embedded tissue
samples, information from MR scans, etc.). This capability may
require that the measurement technique be largely invariant to the
type of sample being indexed, the MR scanner used, as well as the
pulse sequences and the magnitude of the magnetic fields (or the
magnetic-field strengths) used to index the tissue samples or the
information about the individuals. For example, the data structure
may include invariant MR signatures that can be used to generate MR
fingerprints for arbitrary scanning conditions (such as an
arbitrary magnetic field B.sub.0 and an arbitrary pulse sequence),
and the generated MR fingerprints may be compared to a measured MR
fingerprint.
[0245] Note that the biovault may include a set of statistical
definitions of pathology based on research, clinical definitions,
as well as correlations have previous pathological cases used to
compute per pathology risk scores. The pathology risk scores can be
computed for a specific individual for a specific pathology that
includes but is not limited to the statistical probability that the
individual has a specific pathology or is at risk to develop a
specific pathology in the future. The pathology risk scores can be
stored in a look-up table based on the invariant MR signatures.
Alternatively, the pathology risk scores may be stored in a look-up
table based on MR signals, MR spectra and/or MR fingerprints, which
each may be representations or projections of the invariant MR
signatures in particular contexts, such as for a particular MR
scanner having particular characteristics and particular scanning
instructions. Furthermore, the invariant MR signatures may be
linked to specific pathologies and diseases, as determined from
scans of `known good` and `known bad` individuals or tissue
samples, negative and positive-result biopsies, higher-specificity
scans performed around particular or anomalous regions, radiologist
feedback, etc. The biovault can be manually updated by technicians,
researchers, doctors, journals, or other sources. Alternatively or
additionally, the biovault may be automatically updated with
additional tissue-sample or MR-scan information, and/or using a
crawler that analyzes scientific publications and automatically
extracts or scrapes research results and translates them or
integrates them into pathology risk scores.
[0246] In some embodiments, the biovault includes one or more
dimensional animations of a body or a portion of a body over time
(e.g., over weeks, months or years, or during a surgery) based on
multiple invariant MR signatures of an individual that are acquired
at different times.
[0247] The creation of this data structure may aid in the detection
of pathological tissue in vivo or even during scans by allowing the
differences between healthy and unhealthy tissue to be classified
or to identify other anomalous tissue that has not been previously
classified with reduced false-positive rates. (In particular, the
biovault may provide more accurate pathological risk scores because
of its size, with millions or billions of data points, and the
ongoing integrated radiologist feedback, which facilitates
continuous learning/improvement.) This capability may help
determine the portions or regions of an individual that may require
more detailed scans of detected anomalies. For example, an analysis
technique (such as a supervised-learning technique, e.g., a support
vector machine, classification and regression trees, logistic
regression, linear regression, nonlinear regression, a neural
network, a Bayesian technique, etc.) may classify detected
anomalies as healthy or unhealthy tissue based on previous
measurements and classifications in the data structure and features
in MR signals measured in a current scan. Alternatively or
additionally, images may be provided to radiologists or
pathologists who specialize in the type of tissue or the anomaly
detected, and the radiologists or pathologists may confirm the
analysis or may classify the individual.
[0248] In this way, MR signals acquired for tissue in individuals,
whether benign or non-benign, can be indexed, and known-healthy
(e.g., whitelisted tissue) and known-anomalous tissue (e.g.,
blacklisted tissue) can be determined, and unknown tissue in a grey
zone (e.g., greylisted tissue) can be classified. The unknown
tissue may be marked for inspection using other MR techniques,
additional related biopsies, radiologist or pathologist review,
and/or using another analysis technique.
[0249] Note that the invariant MR signatures may be used to improve
detection of anomalies on an individual basis. In particular, what
is normal in one individual may be slightly different than what is
normal in another individual, and clusters of individuals
reflecting various shades or gradations of `normal` can help
classify tissue. (Thus, in some embodiments, the measurement
technique may include an unsupervised-learning technique, such as
clustering, to group or classify similar individuals to facilitate
classification.) Stated differently, the biovault may allow the
information and data for different individuals to be interpreted in
their medical context (such as a person's past injuries,
activities, and environment), thereby increasing the accuracy of
pathological risk scores that are computed for these individuals.
As noted previously, the amount of data that can be captured about
each individual may be much larger than the amount of data that can
be processed by a single pathologist or radiologist or even a team
of radiologists and pathologists. The invariant MR signatures in
the biovault may be used to compensate for or eliminate this
limitation or constraint.
[0250] As more individuals are scanned and indexed, the biovault
will include an ever larger knowledge base of tissue
characteristics and structures in the body. This will allow the
system to model individuals' bodies, at the voxel level, in
multiple dimensions (including tissue and chemical characteristics)
as a function of time. In addition, the system will be able to
combine the models for different individuals into meta-models
(i.e., aggregated models for multiple individuals) that are
segmented based on age, gender and other factors in the biovault to
accurately determine anomalies and pathological risk scores. For
example, when indexing a specific region of an individual's brain,
if a chemical signature exists in a concentration more than 3
standard deviations outside the average concentration against a
million individuals of the same age and gender it may indicate an
anomaly. More sophisticated models may be used to define an
anomaly, and these models may be organ or region specific in the
body and/or for a subset of the population. Thus, over time, the
system may be able to automatically cluster individuals into
subpopulations that accurately predict the risk of different
pathologies. In particular, given a large enough body of data, the
system may be able to determine an individual's risk for developing
arthritis in their knee by correlating cartilage degeneration with
people that may have larger than average differences in the length
of their femur or tibia. Such correlations have typically not been
determined previously on a large scale because the data did not
exist to do so. Therefore, the biovault may open up a new era in
understanding of the human body and may result in a number of
health applications that will improve the quality of life for many
people.
[0251] In some embodiments, the invariant MR signature from a
previous MR scan of an individual (or a related or similar
individual) is used as a target for comparison to the MR signals
during a current scan of the individual. For example, the previous
invariant MR signature may be used to generate estimated or
simulated MR signals for voxels in an individual in the current
scan based on the characteristics of an MR scanner and/or the
scanning instructions. In particular, the previous invariant MR
signature may include or may specify parameters in an MR model that
can be used, in conjunction with the characteristics of an MR
scanner and/or the scanning instructions, to generate the estimated
MR signals. Subsequently, the estimated MR signals can be used as a
target to compare with the MR signals in the current scan. This may
allow rapid identification of areas or regions with unexpected
changes, which may allow identification of the parts or regions of
the individual that may require more detailed scans of detected
anomalies and/or measurement of different parameters (i.e., which
may allow a scan plan to be dynamically updated). This capability
may allow more efficient (i.e., faster) and more accurate scans of
the individual, such as by allowing: different scanning
instructions, different MR techniques, and/or different voxels
sizes to be used in different portions or regions of the individual
(e.g., larger voxels sizes in less interesting regions and smaller
voxel sizes in regions that require more detailed scans).
[0252] Thus, the measurement technique may allow hospitals and
research institutions to catalogue and index many or even all of
the MR signals associated with different individuals in a
searchable way, and may allow a large data structure of indexed
symptomatic and asymptomatic individuals to be amassed and used in
an efficient manner (i.e., the measurement technique may be scaled
to a large number of individuals) to provide clinically relevant
results.
[0253] For example, when a region of interest is identified in an
individual (manually by an operator or technician and/or
automatically based on comparisons with simulated or estimated MR
signals based on previous invariant MR signatures for this
individual), a search may be automatically performed against the
stored invariant MR signatures for other individuals and/or
clinical research that have similar region(s) based on tissue
parameters in the region of interest These searches may surface
similar cases and outcomes, with known diagnoses, to a radiologist
analyzing the measurements on the individual.
[0254] While the preceding discussion illustrated the use of MR
techniques in the measurement technique, this approach may be
generalized to a measurement system that is able to physically
model and measure a material in real-time using a wide variety of
measurement techniques (including one or more of the other
measurements performed on the individual). In general, this
measurement system can use a combination of mechanical and/or
electromagnetic waves to `perturb` the volume being scanned in
order to evaluate the correctness of a prediction in terms of how
the volume will respond to these perturbations. This also includes
the ability for the measurement system to simulate itself and any
part of the environment in which the measurement system is located
that could affect the correctness of the predictive model the
measurement system is trying to generate to describe the volume
being scanned.
[0255] Note that the different measurement techniques may provide
tensor-field mapping and the ability to detect anomalies in tensor
fields. These maps can be images or quantitative tensor field maps,
and each of the measurements technique may provide a visualization
of a different type of tensor field map captured with a different
measurement technique. By looking at or considering two or more of
these maps, of the measurement system may have access to orthogonal
information.
[0256] Thus, the measurement system may provide a way to capture,
in real-time or near real-time, higher-order or hyper-dimensional
pseudo-tensors or matrices at each voxel in 3D space. Using
electromagnetic and/or mechanical perturbations, the measurement
system may use different measurement techniques to measure
disturbances and responses, and then to simulate the responses.
Moreover, the measurement system may iterate this process based on
differences between the measured and the simulated responses. For
example, during the iteration, the sampling frequency, the
measurement technique, etc. may be modified to determine additional
information that is subsequently used to refine the simulations and
to reduce the differences. Stated differently, the next
perturbation or disturbance may be chosen to minimize the error of
the difference across the hyper-dimensional space. Note that this
adaptation or learning may be based on one or more supervised
learning techniques (such as a deep-learning technique) and/or a
non-deterministic approach (such as a heuristic).
[0257] Consequently, the hyper-dimensional matrices at the voxels
may not have a fixed resolution and/or a fixed set of captured
parameters. Instead, this information (such as a sparsity of the
matrices) may vary based on the results of previous scans and/or a
current scan. For example, coarse scans may be followed by
fine-resolutions scans of particular regions or features that are
of interest based on constraints, such as a prior knowledge (e.g.,
a medical history of one or more individuals, etc.).
[0258] The result of this characterization may be a (4+N)D (three
spatial dimensions, one time dimension, and N measurement
dimensions at each point in space) quantitative model of the volume
being scanned. Thus, the measurement technique may involve MR
techniques other than MRI or may include MRI. Note that the (4+N)D
quantitative model may be projected onto an arbitrary subset of the
full (4+N)D space, including 2D or 3D images.
[0259] In some embodiments, the use of multidimensional data and
models provides enhanced diagnostic accuracy (i.e., a lower
false-positive rate) relative to conventional MRI approaches, even
if a larger voxel size is used. Thus, the measurement technique may
allow improved diagnostic accuracy with a larger voxel size than
would be needed in conventional MRI.
[0260] Note that the multi-dimensional data (such as the MR models)
in the biovault may be used for a variety of purposes. For example,
a 3D model of an individual may be used as a reference about the
structure of the individual's body, such that, if the individual
suffers a fracture or a broken bone, the 3D model may be used to
guide 3D printing or customization of a cast or a replacement part
or insert. Because such a cast or insert may fit the individual's
anatomy quite well, it may provide faster and improved healing and,
thus, improved long-term mobility.
[0261] We now further describe an electronic device that performs
at least some of the operations in measurement technique. FIG. 8
presents a block diagram illustrating an example of an electronic
device 800 in system 100 (FIG. 1), such as computer system 114
(FIG. 1) or another of the computer-controlled components in system
100 (FIG. 1). This electronic device includes a processing
subsystem 810, memory subsystem 812, and networking subsystem 814.
Processing subsystem 810 may include one or more devices configured
to perform computational operations and to control components in
system 100 (FIG. 1). For example, processing subsystem 810 may
include one or more microprocessors, one or more graphics
processing units (GPUs), application-specific integrated circuits
(ASICs), microcontrollers, programmable-logic devices, and/or one
or more digital signal processors (DSPs).
[0262] Memory subsystem 812 may include one or more devices for
storing data and/or instructions for processing subsystem 810 and
networking subsystem 814. For example, memory subsystem 812 may
include dynamic random access memory (DRAM), static random access
memory (SRAM), and/or other types of memory. In some embodiments,
instructions for processing subsystem 810 in memory subsystem 812
include one or more program modules 824 or sets of instructions,
which may be executed in an operating environment (such as
operating system 822) by processing subsystem 810. Note that the
one or more computer programs may constitute a computer-program
mechanism or a program module (i.e., software). Moreover,
instructions in the various modules in memory subsystem 812 may be
implemented in: a high-level procedural language, an
object-oriented programming language, and/or in an assembly or
machine language. Furthermore, the programming language may be
compiled or interpreted, e.g., configurable or configured (which
may be used interchangeably in this discussion), to be executed by
processing subsystem 810.
[0263] In addition, memory subsystem 812 may include mechanisms for
controlling access to the memory. In some embodiments, memory
subsystem 812 includes a memory hierarchy that comprises one or
more caches coupled to a memory in electronic device 800. In some
of these embodiments, one or more of the caches is located in
processing subsystem 810.
[0264] In some embodiments, memory subsystem 812 is coupled to one
or more high-capacity mass-storage devices (not shown). For
example, memory subsystem 812 may be coupled to a magnetic or
optical drive, a solid-state drive, or another type of mass-storage
device. In these embodiments, memory subsystem 812 may be used by
electronic device 800 as fast-access storage for often-used data,
while the mass-storage device is used to store less frequently used
data.
[0265] In some embodiments, memory subsystem 812 includes a
remotely located archive device. This archive device can be a
high-capacity network attached mass-storage device, such as:
network attached storage (NAS), an external hard drive, a storage
server, a cluster of servers, a cloud-storage provider, a
cloud-computing provider, a magnetic-tape backup system, a medical
records archive service, and/or another type of archive device.
Moreover, processing subsystem 810 may interact with the archive
device via an application programming interface to store and/or
access information from the archive device. Note that memory
subsystem 812 and/or electronic device 800 may be compliant with
the Health Insurance Portability and Accountability Act.
[0266] An example of the data stored (locally and/or remotely) in
memory subsystem 812 is shown in FIG. 9, which presents a drawing
illustrating an example of a data structure 900 that is used by
electronic device 800 (FIG. 8). This data structure may include: an
identifier 910-1 of individual 908-1, label information 912 (such
as age, gender, biopsy results and diagnosis if one has already
been made and/or any other suitable sample information), timestamps
914 when data was acquired, received MR signals 916 (and, more
generally, raw data), MR capture and model parameters 918
(including the voxel size, speed, resonant frequency, T.sub.1 and
T.sub.2 relaxation times, signal processing techniques, RF pulse
techniques, magnetic gradient strengths, the variable magnetic
field B.sub.0, the pulse sequence, etc.), metadata 920 (such as
information characterizing individual 908-1, demographic
information, family history, optional segmentation data, data
generated from or in response to the raw data, etc.), environmental
conditions 922 (such as the temperature, humidity and/or barometric
pressure in the room or the chamber in which individual 908-1 was
measured), a determined invariant MR signature 924, one or more
additional measurements 926 of physical properties of individual
908-1 (such as weight, dimensions, images, etc.), transformed data
928 generated from or in response to MR signals 916 (such as an
estimated invariant MR signature), optional detected anomalies 930
(which, for a particular voxel, may include information specifying
one or more of detected anomalies 930), optional classifications
932 of detected anomalies 930), registration information 934 and/or
segmentation information 936. Note that data structure 900 may
include multiple entries for different scanning instructions.
[0267] In one embodiment, data in data structure 900 is encrypted
using a block-chain or a similar cryptographic hash technique to
detect unauthorized modification or corruption of records.
Moreover, the data can be anonymized prior to storage so that the
identity of an individual is anonymous unless the individual gives
permission or authorization to access or release the individual's
identity.
[0268] Referring back to FIG. 8, networking subsystem 814 may
include one or more devices configured to couple to and communicate
on a wired, optical and/or wireless network (i.e., to perform
network operations and, more generally, communication), including:
control logic 816, an interface circuit 818, one or more antennas
820 and/or input/output (I/O) port 828. (While FIG. 8 includes one
or more antennas 820, in some embodiments electronic device 800
includes one or more nodes 808, e.g., a pad or connector, which can
be coupled to one or more antennas 820. Thus, electronic device 800
may or may not include one or more antennas 820.) For example,
networking subsystem 814 can include a Bluetooth networking system
(which can include Bluetooth Low Energy, BLE or Bluetooth LE), a
cellular networking system (e.g., a 3G/4G network such as UMTS,
LTE, etc.), a universal serial bus (USB) networking system, a
networking system based on the standards described in IEEE 802.11
(e.g., a Wi-Fi networking system), an Ethernet networking system,
and/or another networking system.
[0269] Moreover, networking subsystem 814 may include processors,
controllers, radios/antennas, sockets/plugs, and/or other devices
used for coupling to, communicating on, and handling data and
events for each supported networking system. Note that mechanisms
used for coupling to, communicating on, and handling data and
events on the network for each network system are sometimes
collectively referred to as a `network interface` for network
subsystem 814. Moreover, in some embodiments a `network` between
components in system 100 (FIG. 1) does not yet exist. Therefore,
electronic device 800 may use the mechanisms in networking
subsystem 814 for performing simple wireless communication between
the components, e.g., transmitting advertising or beacon frames
and/or scanning for advertising frames transmitted by other
components.
[0270] Within electronic device 800, processing subsystem 810,
memory subsystem 812, networking subsystem 814 may be coupled using
one or more interconnects, such as bus 826. These interconnects may
include an electrical, optical, and/or electro-optical connection
that the subsystems can use to communicate commands and data among
one another. Although only one bus 826 is shown for clarity,
different embodiments can include a different number or
configuration of electrical, optical, and/or electro-optical
connections among the subsystems.
[0271] Electronic device 800 may be (or can be) included in a wide
variety of electronic devices. For example, electronic device 800
may be included in: a tablet computer, a smartphone, a smartwatch,
a portable computing device, test equipment, a digital signal
processor, a cluster of computing devices, a laptop computer, a
desktop computer, a server, a subnotebook/netbook and/or another
computing device.
[0272] Although specific components are used to describe electronic
device 800, in alternative embodiments, different components and/or
subsystems may be present in electronic device 800. For example,
electronic device 800 may include one or more additional processing
subsystems, memory subsystems, and/or networking subsystems.
Additionally, one or more of the subsystems may not be present in
electronic device 800. Moreover, in some embodiments, electronic
device 800 may include one or more additional subsystems that are
not shown in FIG. 8.
[0273] Although separate subsystems are shown in FIG. 8, in some
embodiments, some or all of a given subsystem or component can be
integrated into one or more of the other subsystems or components
in electronic device 800. For example, in some embodiments the one
or more program modules 824 are included in operating system 822.
In some embodiments, a component in a given subsystem is included
in a different subsystem. Furthermore, in some embodiments
electronic device 800 is located at a single geographic location or
is distributed over multiple different geographic locations.
[0274] Moreover, the circuits and components in electronic device
800 may be implemented using any combination of analog and/or
digital circuitry, including: bipolar, PMOS and/or NMOS gates or
transistors. Furthermore, signals in these embodiments may include
digital signals that have approximately discrete values and/or
analog signals that have continuous values. Additionally,
components and circuits may be single-ended or differential, and
power supplies may be unipolar or bipolar.
[0275] An integrated circuit may implement some or all of the
functionality of networking subsystem 814 (such as a radio) and,
more generally, some or all of the functionality of electronic
device 800. Moreover, the integrated circuit may include hardware
and/or software mechanisms that are used for transmitting wireless
signals from electronic device 800 and receiving signals at
electronic device 800 from other components in system 100 (FIG. 1)
and/or from electronic devices outside of system 100 (FIG. 1).
Aside from the mechanisms herein described, radios are generally
known in the art and hence are not described in detail. In general,
networking subsystem 814 and/or the integrated circuit can include
any number of radios. Note that the radios in multiple-radio
embodiments function in a similar way to the radios described in
single-radio embodiments.
[0276] While some of the operations in the preceding embodiments
were implemented in hardware or software, in general the operations
in the preceding embodiments can be implemented in a wide variety
of configurations and architectures. Therefore, some or all of the
operations in the preceding embodiments may be performed in
hardware, in software or both.
[0277] In addition, in some of the preceding embodiments there are
fewer components, more components, a position of a component is
changed and/or two or more components are combined.
[0278] In the preceding description, we refer to `some
embodiments.` Note that `some embodiments` describes a subset of
all of the possible embodiments, but does not always specify the
same subset of embodiments.
[0279] While the preceding discussion used one or more MR
techniques as an illustrative example, in other embodiments the
measurement technique is used in conjunction with one or more
alternative or additional non-invasive imaging or measurement
techniques, such as: computed tomography, ultrasound, x-ray,
positron emission spectroscopy, electron spin resonance,
optical/infrared spectroscopy (e.g., to determine a complex index
of refraction at one or more wavelengths), electrical impedance at
DC and/or an AC frequency, proton beam, photoacoustic, etc. In
particular, using one or more of these alternative or additional
non-invasive imaging or measurement techniques, quantitative
comparisons of non-invasive imaging or measurements and simulated
or computed measurements may be used to iteratively update
measurement instructions and/or to detect potential anomalies.
[0280] The foregoing description is intended to enable any person
skilled in the art to make and use the disclosure, and is provided
in the context of a particular application and its requirements.
Moreover, the foregoing descriptions of embodiments of the present
disclosure have been presented for purposes of illustration and
description only. They are not intended to be exhaustive or to
limit the present disclosure to the forms disclosed. Accordingly,
many modifications and variations will be apparent to practitioners
skilled in the art, and the general principles defined herein may
be applied to other embodiments and applications without departing
from the spirit and scope of the present disclosure. Additionally,
the discussion of the preceding embodiments is not intended to
limit the present disclosure. Thus, the present disclosure is not
intended to be limited to the embodiments shown, but is to be
accorded the widest scope consistent with the principles and
features disclosed herein.
* * * * *